@techreport{GrigorjewMetzgerHossfeldetal.2020, author = {Grigorjew, Alexej and Metzger, Florian and Hoßfeld, Tobias and Specht, Johannes and G{\"o}tz, Franz-Josef and Chen, Feng and Schmitt, J{\"u}rgen}, title = {Asynchronous Traffic Shaping with Jitter Control}, doi = {10.25972/OPUS-20582}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-205824}, pages = {8}, year = {2020}, abstract = {Asynchronous Traffic Shaping enabled bounded latency with low complexity for time sensitive networking without the need for time synchronization. However, its main focus is the guaranteed maximum delay. Jitter-sensitive applications may still be forced towards synchronization. This work proposes traffic damping to reduce end-to-end delay jitter. It discusses its application and shows that both the prerequisites and the guaranteed delay of traffic damping and ATS are very similar. Finally, it presents a brief evaluation of delay jitter in an example topology by means of a simulation and worst case estimation.}, subject = {Echtzeit}, language = {en} } @phdthesis{Ifflaender2021, author = {Iffl{\"a}nder, Lukas}, title = {Attack-aware Security Function Management}, doi = {10.25972/OPUS-22421}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-224211}, school = {Universit{\"a}t W{\"u}rzburg}, year = {2021}, abstract = {Over the last decades, cybersecurity has become an increasingly important issue. Between 2019 and 2011 alone, the losses from cyberattacks in the United States grew by 6217\%. At the same time, attacks became not only more intensive but also more and more versatile and diverse. Cybersecurity has become everyone's concern. Today, service providers require sophisticated and extensive security infrastructures comprising many security functions dedicated to various cyberattacks. Still, attacks become more violent to a level where infrastructures can no longer keep up. Simply scaling up is no longer sufficient. To address this challenge, in a whitepaper, the Cloud Security Alliance (CSA) proposed multiple work packages for security infrastructure, leveraging the possibilities of Software-defined Networking (SDN) and Network Function Virtualization (NFV). Security functions require a more sophisticated modeling approach than regular network functions. Notably, the property to drop packets deemed malicious has a significant impact on Security Service Function Chains (SSFCs)—service chains consisting of multiple security functions to protect against multiple at- tack vectors. Under attack, the order of these chains influences the end-to-end system performance depending on the attack type. Unfortunately, it is hard to predict the attack composition at system design time. Thus, we make a case for dynamic attack-aware SSFC reordering. Also, we tackle the issues of the lack of integration between security functions and the surrounding network infrastructure, the insufficient use of short term CPU frequency boosting, and the lack of Intrusion Detection and Prevention Systems (IDPS) against database ransomware attacks. Current works focus on characterizing the performance of security functions and their behavior under overload without considering the surrounding infrastructure. Other works aim at replacing security functions using network infrastructure features but do not consider integrating security functions within the network. Further publications deal with using SDN for security or how to deal with new vulnerabilities introduced through SDN. However, they do not take security function performance into account. NFV is a popular field for research dealing with frameworks, benchmarking methods, the combination with SDN, and implementing security functions as Virtualized Network Functions (VNFs). Research in this area brought forth the concept of Service Function Chains (SFCs) that chain multiple network functions after one another. Nevertheless, they still do not consider the specifics of security functions. The mentioned CSA whitepaper proposes many valuable ideas but leaves their realization open to others. This thesis presents solutions to increase the performance of single security functions using SDN, performance modeling, a framework for attack-aware SSFC reordering, a solution to make better use of CPU frequency boosting, and an IDPS against database ransomware. Specifically, the primary contributions of this work are: • We present approaches to dynamically bypass Intrusion Detection Systems (IDS) in order to increase their performance without reducing the security level. To this end, we develop and implement three SDN-based approaches (two dynamic and one static). We evaluate the proposed approaches regarding security and performance and show that they significantly increase the performance com- pared to an inline IDS without significant security deficits. We show that using software switches can further increase the performance of the dynamic approaches up to a point where they can eliminate any throughput drawbacks when using the IDS. • We design a DDoS Protection System (DPS) against TCP SYN flood at tacks in the form of a VNF that works inside an SDN-enabled network. This solution eliminates known scalability and performance drawbacks of existing solutions for this attack type. Then, we evaluate this solution showing that it correctly handles the connection establishment and present solutions for an observed issue. Next, we evaluate the performance showing that our solution increases performance up to three times. Parallelization and parameter tuning yields another 76\% performance boost. Based on these findings, we discuss optimal deployment strategies. • We introduce the idea of attack-aware SSFC reordering and explain its impact in a theoretical scenario. Then, we discuss the required information to perform this process. We validate our claim of the importance of the SSFC order by analyzing the behavior of single security functions and SSFCs. Based on the results, we conclude that there is a massive impact on the performance up to three orders of magnitude, and we find contradicting optimal orders for different workloads. Thus, we demonstrate the need for dynamic reordering. Last, we develop a model for SSFC regarding traffic composition and resource demands. We classify the traffic into multiple classes and model the effect of single security functions on the traffic and their generated resource demands as functions of the incoming network traffic. Based on our model, we propose three approaches to determine optimal orders for reordering. • We implement a framework for attack-aware SSFC reordering based on this knowledge. The framework places all security functions inside an SDN-enabled network and reorders them using SDN flows. Our evaluation shows that the framework can enforce all routes as desired. It correctly adapts to all attacks and returns to the original state after the attacks cease. We find possible security issues at the moment of reordering and present solutions to eliminate them. • Next, we design and implement an approach to load balance servers while taking into account their ability to go into a state of Central Processing Unit (CPU) frequency boost. To this end, the approach collects temperature information from available hosts and places services on the host that can attain the boosted mode the longest. We evaluate this approach and show its effectiveness. For high load scenarios, the approach increases the overall performance and the performance per watt. Even better results show up for low load workloads, where not only all performance metrics improve but also the temperatures and total power consumption decrease. • Last, we design an IDPS protecting against database ransomware attacks that comprise multiple queries to attain their goal. Our solution models these attacks using a Colored Petri Net (CPN). A proof-of-concept implementation shows that our approach is capable of detecting attacks without creating false positives for benign scenarios. Furthermore, our solution creates only a small performance impact. Our contributions can help to improve the performance of security infrastructures. We see multiple application areas from data center operators over software and hardware developers to security and performance researchers. Most of the above-listed contributions found use in several research publications. Regarding future work, we see the need to better integrate SDN-enabled security functions and SSFC reordering in data center networks. Future SSFC should discriminate between different traffic types, and security frameworks should support automatically learning models for security functions. We see the need to consider energy efficiency when regarding SSFCs and take CPU boosting technologies into account when designing performance models as well as placement, scaling, and deployment strategies. Last, for a faster adaptation against recent ransomware attacks, we propose machine-assisted learning for database IDPS signatures.}, subject = {Software-defined networking}, language = {en} } @article{WolffRutter2012, author = {Wolff, Alexander and Rutter, Iganz}, title = {Augmenting the Connectivity of Planar and Geometric Graphs}, series = {Journal of Graph Algorithms and Applications}, journal = {Journal of Graph Algorithms and Applications}, doi = {10.7155/jgaa.00275}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-97587}, year = {2012}, abstract = {In this paper we study connectivity augmentation problems. Given a connected graph G with some desirable property, we want to make G 2-vertex connected (or 2-edge connected) by adding edges such that the resulting graph keeps the property. The aim is to add as few edges as possible. The property that we consider is planarity, both in an abstract graph-theoretic and in a geometric setting, where vertices correspond to points in the plane and edges to straight-line segments. We show that it is NP-hard to � nd a minimum-cardinality augmentation that makes a planar graph 2-edge connected. For making a planar graph 2-vertex connected this was known. We further show that both problems are hard in the geometric setting, even when restricted to trees. The problems remain hard for higher degrees of connectivity. On the other hand we give polynomial-time algorithms for the special case of convex geometric graphs. We also study the following related problem. Given a planar (plane geometric) graph G, two vertices s and t of G, and an integer c, how many edges have to be added to G such that G is still planar (plane geometric) and contains c edge- (or vertex-) disjoint s{t paths? For the planar case we give a linear-time algorithm for c = 2. For the plane geometric case we give optimal worst-case bounds for c = 2; for c = 3 we characterize the cases that have a solution.}, language = {en} } @article{KrenzerHeilFittingetal., author = {Krenzer, Adrian and Heil, Stefan and Fitting, Daniel and Matti, Safa and Zoller, Wolfram G. and Hann, Alexander and Puppe, Frank}, title = {Automated classification of polyps using deep learning architectures and few-shot learning}, series = {BMC Medical Imaging}, volume = {23}, journal = {BMC Medical Imaging}, doi = {10.1186/s12880-023-01007-4}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-357465}, abstract = {Background Colorectal cancer is a leading cause of cancer-related deaths worldwide. The best method to prevent CRC is a colonoscopy. However, not all colon polyps have the risk of becoming cancerous. Therefore, polyps are classified using different classification systems. After the classification, further treatment and procedures are based on the classification of the polyp. Nevertheless, classification is not easy. Therefore, we suggest two novel automated classifications system assisting gastroenterologists in classifying polyps based on the NICE and Paris classification. Methods We build two classification systems. One is classifying polyps based on their shape (Paris). The other classifies polyps based on their texture and surface patterns (NICE). A two-step process for the Paris classification is introduced: First, detecting and cropping the polyp on the image, and secondly, classifying the polyp based on the cropped area with a transformer network. For the NICE classification, we design a few-shot learning algorithm based on the Deep Metric Learning approach. The algorithm creates an embedding space for polyps, which allows classification from a few examples to account for the data scarcity of NICE annotated images in our database. Results For the Paris classification, we achieve an accuracy of 89.35 \%, surpassing all papers in the literature and establishing a new state-of-the-art and baseline accuracy for other publications on a public data set. For the NICE classification, we achieve a competitive accuracy of 81.13 \% and demonstrate thereby the viability of the few-shot learning paradigm in polyp classification in data-scarce environments. Additionally, we show different ablations of the algorithms. Finally, we further elaborate on the explainability of the system by showing heat maps of the neural network explaining neural activations. Conclusion Overall we introduce two polyp classification systems to assist gastroenterologists. We achieve state-of-the-art performance in the Paris classification and demonstrate the viability of the few-shot learning paradigm in the NICE classification, addressing the prevalent data scarcity issues faced in medical machine learning.}, language = {en} } @phdthesis{Bauer2021, author = {Bauer, Andr{\´e}}, title = {Automated Hybrid Time Series Forecasting: Design, Benchmarking, and Use Cases}, doi = {10.25972/OPUS-22025}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-220255}, school = {Universit{\"a}t W{\"u}rzburg}, year = {2021}, abstract = {These days, we are living in a digitalized world. Both our professional and private lives are pervaded by various IT services, which are typically operated using distributed computing systems (e.g., cloud environments). Due to the high level of digitalization, the operators of such systems are confronted with fast-paced and changing requirements. In particular, cloud environments have to cope with load fluctuations and respective rapid and unexpected changes in the computing resource demands. To face this challenge, so-called auto-scalers, such as the threshold-based mechanism in Amazon Web Services EC2, can be employed to enable elastic scaling of the computing resources. However, despite this opportunity, business-critical applications are still run with highly overprovisioned resources to guarantee a stable and reliable service operation. This strategy is pursued due to the lack of trust in auto-scalers and the concern that inaccurate or delayed adaptations may result in financial losses. To adapt the resource capacity in time, the future resource demands must be "foreseen", as reacting to changes once they are observed introduces an inherent delay. In other words, accurate forecasting methods are required to adapt systems proactively. A powerful approach in this context is time series forecasting, which is also applied in many other domains. The core idea is to examine past values and predict how these values will evolve as time progresses. According to the "No-Free-Lunch Theorem", there is no algorithm that performs best for all scenarios. Therefore, selecting a suitable forecasting method for a given use case is a crucial task. Simply put, each method has its benefits and drawbacks, depending on the specific use case. The choice of the forecasting method is usually based on expert knowledge, which cannot be fully automated, or on trial-and-error. In both cases, this is expensive and prone to error. Although auto-scaling and time series forecasting are established research fields, existing approaches cannot fully address the mentioned challenges: (i) In our survey on time series forecasting, we found that publications on time series forecasting typically consider only a small set of (mostly related) methods and evaluate their performance on a small number of time series with only a few error measures while providing no information on the execution time of the studied methods. Therefore, such articles cannot be used to guide the choice of an appropriate method for a particular use case; (ii) Existing open-source hybrid forecasting methods that take advantage of at least two methods to tackle the "No-Free-Lunch Theorem" are computationally intensive, poorly automated, designed for a particular data set, or they lack a predictable time-to-result. Methods exhibiting a high variance in the time-to-result cannot be applied for time-critical scenarios (e.g., auto-scaling), while methods tailored to a specific data set introduce restrictions on the possible use cases (e.g., forecasting only annual time series); (iii) Auto-scalers typically scale an application either proactively or reactively. Even though some hybrid auto-scalers exist, they lack sophisticated solutions to combine reactive and proactive scaling. For instance, resources are only released proactively while resource allocation is entirely done in a reactive manner (inherently delayed); (iv) The majority of existing mechanisms do not take the provider's pricing scheme into account while scaling an application in a public cloud environment, which often results in excessive charged costs. Even though some cost-aware auto-scalers have been proposed, they only consider the current resource demands, neglecting their development over time. For example, resources are often shut down prematurely, even though they might be required again soon. To address the mentioned challenges and the shortcomings of existing work, this thesis presents three contributions: (i) The first contribution-a forecasting benchmark-addresses the problem of limited comparability between existing forecasting methods; (ii) The second contribution-Telescope-provides an automated hybrid time series forecasting method addressing the challenge posed by the "No-Free-Lunch Theorem"; (iii) The third contribution-Chamulteon-provides a novel hybrid auto-scaler for coordinated scaling of applications comprising multiple services, leveraging Telescope to forecast the workload intensity as a basis for proactive resource provisioning. In the following, the three contributions of the thesis are summarized: Contribution I - Forecasting Benchmark To establish a level playing field for evaluating the performance of forecasting methods in a broad setting, we propose a novel benchmark that automatically evaluates and ranks forecasting methods based on their performance in a diverse set of evaluation scenarios. The benchmark comprises four different use cases, each covering 100 heterogeneous time series taken from different domains. The data set was assembled from publicly available time series and was designed to exhibit much higher diversity than existing forecasting competitions. Besides proposing a new data set, we introduce two new measures that describe different aspects of a forecast. We applied the developed benchmark to evaluate Telescope. Contribution II - Telescope To provide a generic forecasting method, we introduce a novel machine learning-based forecasting approach that automatically retrieves relevant information from a given time series. More precisely, Telescope automatically extracts intrinsic time series features and then decomposes the time series into components, building a forecasting model for each of them. Each component is forecast by applying a different method and then the final forecast is assembled from the forecast components by employing a regression-based machine learning algorithm. In more than 1300 hours of experiments benchmarking 15 competing methods (including approaches from Uber and Facebook) on 400 time series, Telescope outperformed all methods, exhibiting the best forecast accuracy coupled with a low and reliable time-to-result. Compared to the competing methods that exhibited, on average, a forecast error (more precisely, the symmetric mean absolute forecast error) of 29\%, Telescope exhibited an error of 20\% while being 2556 times faster. In particular, the methods from Uber and Facebook exhibited an error of 48\% and 36\%, and were 7334 and 19 times slower than Telescope, respectively. Contribution III - Chamulteon To enable reliable auto-scaling, we present a hybrid auto-scaler that combines proactive and reactive techniques to scale distributed cloud applications comprising multiple services in a coordinated and cost-effective manner. More precisely, proactive adaptations are planned based on forecasts of Telescope, while reactive adaptations are triggered based on actual observations of the monitored load intensity. To solve occurring conflicts between reactive and proactive adaptations, a complex conflict resolution algorithm is implemented. Moreover, when deployed in public cloud environments, Chamulteon reviews adaptations with respect to the cloud provider's pricing scheme in order to minimize the charged costs. In more than 400 hours of experiments evaluating five competing auto-scaling mechanisms in scenarios covering five different workloads, four different applications, and three different cloud environments, Chamulteon exhibited the best auto-scaling performance and reliability while at the same time reducing the charged costs. The competing methods provided insufficient resources for (on average) 31\% of the experimental time; in contrast, Chamulteon cut this time to 8\% and the SLO (service level objective) violations from 18\% to 6\% while using up to 15\% less resources and reducing the charged costs by up to 45\%. The contributions of this thesis can be seen as major milestones in the domain of time series forecasting and cloud resource management. (i) This thesis is the first to present a forecasting benchmark that covers a variety of different domains with a high diversity between the analyzed time series. Based on the provided data set and the automatic evaluation procedure, the proposed benchmark contributes to enhance the comparability of forecasting methods. The benchmarking results for different forecasting methods enable the selection of the most appropriate forecasting method for a given use case. (ii) Telescope provides the first generic and fully automated time series forecasting approach that delivers both accurate and reliable forecasts while making no assumptions about the analyzed time series. Hence, it eliminates the need for expensive, time-consuming, and error-prone procedures, such as trial-and-error searches or consulting an expert. This opens up new possibilities especially in time-critical scenarios, where Telescope can provide accurate forecasts with a short and reliable time-to-result. Although Telescope was applied for this thesis in the field of cloud computing, there is absolutely no limitation regarding the applicability of Telescope in other domains, as demonstrated in the evaluation. Moreover, Telescope, which was made available on GitHub, is already used in a number of interdisciplinary data science projects, for instance, predictive maintenance in an Industry 4.0 context, heart failure prediction in medicine, or as a component of predictive models of beehive development. (iii) In the context of cloud resource management, Chamulteon is a major milestone for increasing the trust in cloud auto-scalers. The complex resolution algorithm enables reliable and accurate scaling behavior that reduces losses caused by excessive resource allocation or SLO violations. In other words, Chamulteon provides reliable online adaptations minimizing charged costs while at the same time maximizing user experience.}, subject = {Zeitreihenanalyse}, language = {en} } @article{KasparFetteHankeetal.2021, author = {Kaspar, Mathias and Fette, Georg and Hanke, Monika and Ertl, Maximilian and Puppe, Frank and St{\"o}rk, Stefan}, title = {Automated provision of clinical routine data for a complex clinical follow-up study: A data warehouse solution}, series = {Health Informatics Journal}, volume = {28}, journal = {Health Informatics Journal}, number = {1}, doi = {10.1177/14604582211058081}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-260828}, year = {2021}, abstract = {A deep integration of routine care and research remains challenging in many respects. We aimed to show the feasibility of an automated transformation and transfer process feeding deeply structured data with a high level of granularity collected for a clinical prospective cohort study from our hospital information system to the study's electronic data capture system, while accounting for study-specific data and visits. We developed a system integrating all necessary software and organizational processes then used in the study. The process and key system components are described together with descriptive statistics to show its feasibility in general and to identify individual challenges in particular. Data of 2051 patients enrolled between 2014 and 2020 was transferred. We were able to automate the transfer of approximately 11 million individual data values, representing 95\% of all entered study data. These were recorded in n = 314 variables (28\% of all variables), with some variables being used multiple times for follow-up visits. Our validation approach allowed for constant good data quality over the course of the study. In conclusion, the automated transfer of multi-dimensional routine medical data from HIS to study databases using specific study data and visit structures is complex, yet viable.}, language = {en} } @phdthesis{Walter2019, author = {Walter, J{\"u}rgen Christian}, title = {Automation in Software Performance Engineering Based on a Declarative Specification of Concerns}, doi = {10.25972/OPUS-18090}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-180904}, school = {Universit{\"a}t W{\"u}rzburg}, year = {2019}, abstract = {Software performance is of particular relevance to software system design, operation, and evolution because it has a significant impact on key business indicators. During the life-cycle of a software system, its implementation, configuration, and deployment are subject to multiple changes that may affect the end-to-end performance characteristics. Consequently, performance analysts continually need to provide answers to and act based on performance-relevant concerns. To ensure a desired level of performance, software performance engineering provides a plethora of methods, techniques, and tools for measuring, modeling, and evaluating performance properties of software systems. However, the answering of performance concerns is subject to a significant semantic gap between the level on which performance concerns are formulated and the technical level on which performance evaluations are actually conducted. Performance evaluation approaches come with different strengths and limitations concerning, for example, accuracy, time-to-result, or system overhead. For the involved stakeholders, it can be an elaborate process to reasonably select, parameterize and correctly apply performance evaluation approaches, and to filter and interpret the obtained results. An additional challenge is that available performance evaluation artifacts may change over time, which requires to switch between different measurement-based and model-based performance evaluation approaches during the system evolution. At model-based analysis, the effort involved in creating performance models can also outweigh their benefits. To overcome the deficiencies and enable an automatic and holistic evaluation of performance throughout the software engineering life-cycle requires an approach that: (i) integrates multiple types of performance concerns and evaluation approaches, (ii) automates performance model creation, and (iii) automatically selects an evaluation methodology tailored to a specific scenario. This thesis presents a declarative approach —called Declarative Performance Engineering (DPE)— to automate performance evaluation based on a humanreadable specification of performance-related concerns. To this end, we separate the definition of performance concerns from their solution. The primary scientific contributions presented in this thesis are: A declarative language to express performance-related concerns and a corresponding processing framework: We provide a language to specify performance concerns independent of a concrete performance evaluation approach. Besides the specification of functional aspects, the language allows to include non-functional tradeoffs optionally. To answer these concerns, we provide a framework architecture and a corresponding reference implementation to process performance concerns automatically. It allows to integrate arbitrary performance evaluation approaches and is accompanied by reference implementations for model-based and measurement-based performance evaluation. Automated creation of architectural performance models from execution traces: The creation of performance models can be subject to significant efforts outweighing the benefits of model-based performance evaluation. We provide a model extraction framework that creates architectural performance models based on execution traces, provided by monitoring tools.The framework separates the derivation of generic information from model creation routines. To derive generic information, the framework combines state-of-the-art extraction and estimation techniques. We isolate object creation routines specified in a generic model builder interface based on concepts present in multiple performance-annotated architectural modeling formalisms. To create model extraction for a novel performance modeling formalism, developers only need to write object creation routines instead of creating model extraction software from scratch when reusing the generic framework. Automated and extensible decision support for performance evaluation approaches: We present a methodology and tooling for the automated selection of a performance evaluation approach tailored to the user concerns and application scenario. To this end, we propose to decouple the complexity of selecting a performance evaluation approach for a given scenario by providing solution approach capability models and a generic decision engine. The proposed capability meta-model enables to describe functional and non-functional capabilities of performance evaluation approaches and tools at different granularities. In contrast to existing tree-based decision support mechanisms, the decoupling approach allows to easily update characteristics of solution approaches as well as appending new rating criteria and thereby stay abreast of evolution in performance evaluation tooling and system technologies. Time-to-result estimation for model-based performance prediction: The time required to execute a model-based analysis plays an important role in different decision processes. For example, evaluation scenarios might require the prediction results to be available in a limited period of time such that the system can be adapted in time to ensure the desired quality of service. We propose a method to estimate the time-to-result for modelbased performance prediction based on model characteristics and analysis parametrization. We learn a prediction model using performancerelevant features thatwe determined using statistical tests. We implement the approach and demonstrate its practicability by applying it to analyze a simulation-based multi-step performance evaluation approach for a representative architectural performance modeling formalism. We validate each of the contributions based on representative case studies. The evaluation of automatic performance model extraction for two case study systems shows that the resulting models can accurately predict the performance behavior. Prediction accuracy errors are below 3\% for resource utilization and mostly less than 20\% for service response time. The separate evaluation of the reusability shows that the presented approach lowers the implementation efforts for automated model extraction tools by up to 91\%. Based on two case studies applying measurement-based and model-based performance evaluation techniques, we demonstrate the suitability of the declarative performance engineering framework to answer multiple kinds of performance concerns customized to non-functional goals. Subsequently, we discuss reduced efforts in applying performance analyses using the integrated and automated declarative approach. Also, the evaluation of the declarative framework reviews benefits and savings integrating performance evaluation approaches into the declarative performance engineering framework. We demonstrate the applicability of the decision framework for performance evaluation approaches by applying it to depict existing decision trees. Then, we show how we can quickly adapt to the evolution of performance evaluation methods which is challenging for static tree-based decision support systems. At this, we show how to cope with the evolution of functional and non-functional capabilities of performance evaluation software and explain how to integrate new approaches. Finally, we evaluate the accuracy of the time-to-result estimation for a set of machinelearning algorithms and different training datasets. The predictions exhibit a mean percentage error below 20\%, which can be further improved by including performance evaluations of the considered model into the training data. The presented contributions represent a significant step towards an integrated performance engineering process that combines the strengths of model-based and measurement-based performance evaluation. The proposed performance concern language in conjunction with the processing framework significantly reduces the complexity of applying performance evaluations for all stakeholders. Thereby it enables performance awareness throughout the software engineering life-cycle. The proposed performance concern language removes the semantic gap between the level on which performance concerns are formulated and the technical level on which performance evaluations are actually conducted by the user.}, subject = {Software}, language = {en} } @article{BeckerCaminitiFiorellaetal.2013, author = {Becker, Martin and Caminiti, Saverio and Fiorella, Donato and Francis, Louise and Gravino, Pietro and Haklay, Mordechai (Muki) and Hotho, Andreas and Loreto, Virrorio and Mueller, Juergen and Ricchiuti, Ferdinando and Servedio, Vito D. P. and Sirbu, Alina and Tria, Franesca}, title = {Awareness and Learning in Participatory Noise Sensing}, series = {PLOS ONE}, volume = {8}, journal = {PLOS ONE}, number = {12}, issn = {1932-6203}, doi = {10.1371/journal.pone.0081638}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-127675}, pages = {e81638}, year = {2013}, abstract = {The development of ICT infrastructures has facilitated the emergence of new paradigms for looking at society and the environment over the last few years. Participatory environmental sensing, i.e. directly involving citizens in environmental monitoring, is one example, which is hoped to encourage learning and enhance awareness of environmental issues. In this paper, an analysis of the behaviour of individuals involved in noise sensing is presented. Citizens have been involved in noise measuring activities through the WideNoise smartphone application. This application has been designed to record both objective (noise samples) and subjective (opinions, feelings) data. The application has been open to be used freely by anyone and has been widely employed worldwide. In addition, several test cases have been organised in European countries. Based on the information submitted by users, an analysis of emerging awareness and learning is performed. The data show that changes in the way the environment is perceived after repeated usage of the application do appear. Specifically, users learn how to recognise different noise levels they are exposed to. Additionally, the subjective data collected indicate an increased user involvement in time and a categorisation effect between pleasant and less pleasant environments.}, language = {en} } @phdthesis{Dose2021, author = {Dose, Titus}, title = {Balance Problems for Integer Circuits and Separations of Relativized Conjectures on Incompleteness in Promise Classes}, doi = {10.25972/OPUS-22220}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-222209}, school = {Universit{\"a}t W{\"u}rzburg}, year = {2021}, abstract = {This thesis is divided into two parts. In the first part we contribute to a working program initiated by Pudl{\´a}k (2017) who lists several major complexity theoretic conjectures relevant to proof complexity and asks for oracles that separate pairs of corresponding relativized conjectures. Among these conjectures are: - \(\mathsf{CON}\) and \(\mathsf{SAT}\): coNP (resp., NP) does not contain complete sets that have P-optimal proof systems. - \(\mathsf{CON}^{\mathsf{N}}\): coNP does not contain complete sets that have optimal proof systems. - \(\mathsf{TFNP}\): there do not exist complete total polynomial search problems (also known as total NP search problems). - \(\mathsf{DisjNP}\) and \(\mathsf{DisjCoNP}\): There do not exist complete disjoint NP pairs (coNP pairs). - \(\mathsf{UP}\): UP does not contain complete problems. - \(\mathsf{NP}\cap\mathsf{coNP}\): \(\mathrm{NP}\cap\mathrm{coNP}\) does not contain complete problems. - \(\mathrm{P}\ne\mathrm{NP}\). We construct several of the oracles that Pudl{\´a}k asks for. In the second part we investigate the computational complexity of balance problems for \(\{-,\cdot\}\)-circuits computing finite sets of natural numbers (note that \(-\) denotes the set difference). These problems naturally build on problems for integer expressions and integer circuits studied by Stockmeyer and Meyer (1973), McKenzie and Wagner (2007), and Glaßer et al. (2010). Our work shows that the balance problem for \(\{-,\cdot\}\)-circuits is undecidable which is the first natural problem for integer circuits or related constraint satisfaction problems that admits only one arithmetic operation and is proven to be undecidable. Starting from this result we precisely characterize the complexity of balance problems for proper subsets of \(\{-,\cdot\}\). These problems turn out to be complete for one of the classes L, NL, and NP.}, subject = {NP-vollst{\"a}ndiges Problem}, language = {en} } @phdthesis{Oberdoerfer2021, author = {Oberd{\"o}rfer, Sebastian}, title = {Better Learning with Gaming: Knowledge Encoding and Knowledge Learning Using Gamification}, doi = {10.25972/OPUS-21970}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-219707}, school = {Universit{\"a}t W{\"u}rzburg}, year = {2021}, abstract = {Computer games are highly immersive, engaging, and motivating learning environments. By providing a tutorial at the start of a new game, players learn the basics of the game's underlying principles as well as practice how to successfully play the game. During the actual gameplay, players repetitively apply this knowledge, thus improving it due to repetition. Computer games also challenge players with a constant stream of new challenges which increase in difficulty over time. As a result, computer games even require players to transfer their knowledge to master these new challenges. A computer game consists of several game mechanics. Game mechanics are the rules of a computer game and encode the game's underlying principles. They create the virtual environments, generate a game's challenges and allow players to interact with the game. Game mechanics also can encode real world knowledge. This knowledge may be acquired by players via gameplay. However, the actual process of knowledge encoding and knowledge learning using game mechanics has not been thoroughly defined, yet. This thesis therefore proposes a theoretical model to define the knowledge learning using game mechanics: the Gamified Knowledge Encoding. The model is applied to design a serious game for affine transformations, i.e., GEtiT, and to predict the learning outcome of playing a computer game that encodes orbital mechanics in its game mechanics, i.e., Kerbal Space Program. To assess the effects of different visualization technologies on the overall learning outcome, GEtiT visualizes the gameplay in desktop-3D and immersive virtual reality. The model's applicability for effective game design as well as GEtiT's overall design are evaluated in a usability study. The learning outcome of playing GEtiT and Kerbal Space Program is assessed in four additional user studies. The studies' results validate the use of the Gamified Knowledge Encoding for the purpose of developing effective serious games and to predict the learning outcome of existing serious games. GEtiT and Kerbal Space Program yield a similar training effect but a higher motivation to tackle the assignments in comparison to a traditional learning method. In conclusion, this thesis expands the understanding of using game mechanics for an effective learning of knowledge. The presented results are of high importance for researches, educators, and developers as they also provide guidelines for the development of effective serious games.}, subject = {Serious game}, language = {en} } @article{HaunertWolff2017, author = {Haunert, Jan-Henrik and Wolff, Alexander}, title = {Beyond maximum independent set: an extended integer programming formulation for point labeling}, series = {ISPRS International Journal of Geo-Information}, volume = {6}, journal = {ISPRS International Journal of Geo-Information}, number = {11}, doi = {10.3390/ijgi6110342}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-158960}, pages = {342}, year = {2017}, abstract = {Map labeling is a classical problem of cartography that has frequently been approached by combinatorial optimization. Given a set of features in a map and for each feature a set of label candidates, a common problem is to select an independent set of labels (that is, a labeling without label-label intersections) that contains as many labels as possible and at most one label for each feature. To obtain solutions of high cartographic quality, the labels can be weighted and one can maximize the total weight (rather than the number) of the selected labels. We argue, however, that when maximizing the weight of the labeling, the influences of labels on other labels are insufficiently addressed. Furthermore, in a maximum-weight labeling, the labels tend to be densely packed and thus the map background can be occluded too much. We propose extensions of an existing model to overcome these limitations. Since even without our extensions the problem is NP-hard, we cannot hope for an efficient exact algorithm for the problem. Therefore, we present a formalization of our model as an integer linear program (ILP). This allows us to compute optimal solutions in reasonable time, which we demonstrate both for randomly generated point sets and an existing data set of cities. Moreover, a relaxation of our ILP allows for a simple and efficient heuristic, which yielded near-optimal solutions for our instances.}, language = {en} } @article{PfitznerMayNuechter2018, author = {Pfitzner, Christian and May, Stefan and N{\"u}chter, Andreas}, title = {Body weight estimation for dose-finding and health monitoring of lying, standing and walking patients based on RGB-D data}, series = {Sensors}, volume = {18}, journal = {Sensors}, number = {5}, doi = {10.3390/s18051311}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-176642}, pages = {1311}, year = {2018}, abstract = {This paper describes the estimation of the body weight of a person in front of an RGB-D camera. A survey of different methods for body weight estimation based on depth sensors is given. First, an estimation of people standing in front of a camera is presented. Second, an approach based on a stream of depth images is used to obtain the body weight of a person walking towards a sensor. The algorithm first extracts features from a point cloud and forwards them to an artificial neural network (ANN) to obtain an estimation of body weight. Besides the algorithm for the estimation, this paper further presents an open-access dataset based on measurements from a trauma room in a hospital as well as data from visitors of a public event. In total, the dataset contains 439 measurements. The article illustrates the efficiency of the approach with experiments with persons lying down in a hospital, standing persons, and walking persons. Applicable scenarios for the presented algorithm are body weight-related dosing of emergency patients.}, language = {en} } @article{LugrinLatoschikHabeletal.2016, author = {Lugrin, Jean-Luc and Latoschik, Marc Erich and Habel, Michael and Roth, Daniel and Seufert, Christian and Grafe, Silke}, title = {Breaking Bad Behaviors: A New Tool for Learning Classroom Management Using Virtual Reality}, series = {Frontiers in ICT}, volume = {3}, journal = {Frontiers in ICT}, number = {26}, doi = {10.3389/fict.2016.00026}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-147945}, year = {2016}, abstract = {This article presents an immersive virtual reality (VR) system for training classroom management skills, with a specific focus on learning to manage disruptive student behavior in face-to-face, one-to-many teaching scenarios. The core of the system is a real-time 3D virtual simulation of a classroom populated by twenty-four semi-autonomous virtual students. The system has been designed as a companion tool for classroom management seminars in a syllabus for primary and secondary school teachers. This will allow lecturers to link theory with practice using the medium of VR. The system is therefore designed for two users: a trainee teacher and an instructor supervising the training session. The teacher is immersed in a real-time 3D simulation of a classroom by means of a head-mounted display and headphone. The instructor operates a graphical desktop console, which renders a view of the class and the teacher whose avatar movements are captured by a marker less tracking system. This console includes a 2D graphics menu with convenient behavior and feedback control mechanisms to provide human-guided training sessions. The system is built using low-cost consumer hardware and software. Its architecture and technical design are described in detail. A first evaluation confirms its conformance to critical usability requirements (i.e., safety and comfort, believability, simplicity, acceptability, extensibility, affordability, and mobility). Our initial results are promising and constitute the necessary first step toward a possible investigation of the efficiency and effectiveness of such a system in terms of learning outcomes and experience.}, language = {en} } @article{DoellingerWienrichLatoschik2021, author = {D{\"o}llinger, Nina and Wienrich, Carolin and Latoschik, Marc Erich}, title = {Challenges and opportunities of immersive technologies for mindfulness meditation: a systematic review}, series = {Frontiers in Virtual Reality}, volume = {2}, journal = {Frontiers in Virtual Reality}, doi = {10.3389/frvir.2021.644683}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-259047}, pages = {644683}, year = {2021}, abstract = {Mindfulness is considered an important factor of an individual's subjective well-being. Consequently, Human-Computer Interaction (HCI) has investigated approaches that strengthen mindfulness, i.e., by inventing multimedia technologies to support mindfulness meditation. These approaches often use smartphones, tablets, or consumer-grade desktop systems to allow everyday usage in users' private lives or in the scope of organized therapies. Virtual, Augmented, and Mixed Reality (VR, AR, MR; in short: XR) significantly extend the design space for such approaches. XR covers a wide range of potential sensory stimulation, perceptive and cognitive manipulations, content presentation, interaction, and agency. These facilities are linked to typical XR-specific perceptions that are conceptually closely related to mindfulness research, such as (virtual) presence and (virtual) embodiment. However, a successful exploitation of XR that strengthens mindfulness requires a systematic analysis of the potential interrelation and influencing mechanisms between XR technology, its properties, factors, and phenomena and existing models and theories of the construct of mindfulness. This article reports such a systematic analysis of XR-related research from HCI and life sciences to determine the extent to which existing research frameworks on HCI and mindfulness can be applied to XR technologies, the potential of XR technologies to support mindfulness, and open research gaps. Fifty papers of ACM Digital Library and National Institutes of Health's National Library of Medicine (PubMed) with and without empirical efficacy evaluation were included in our analysis. The results reveal that at the current time, empirical research on XR-based mindfulness support mainly focuses on therapy and therapeutic outcomes. Furthermore, most of the currently investigated XR-supported mindfulness interactions are limited to vocally guided meditations within nature-inspired virtual environments. While an analysis of empirical research on those systems did not reveal differences in mindfulness compared to non-mediated mindfulness practices, various design proposals illustrate that XR has the potential to provide interactive and body-based innovations for mindfulness practice. We propose a structured approach for future work to specify and further explore the potential of XR as mindfulness-support. The resulting framework provides design guidelines for XR-based mindfulness support based on the elements and psychological mechanisms of XR interactions.}, language = {en} } @techreport{NguyenLohHossfeld2023, type = {Working Paper}, author = {Nguyen, Kien and Loh, Frank and Hoßfeld, Tobias}, title = {Challenges of Serverless Deployment in Edge-MEC-Cloud}, series = {KuVS Fachgespr{\"a}ch - W{\"u}rzburg Workshop on Modeling, Analysis and Simulation of Next-Generation Communication Networks 2023 (WueWoWAS'23)}, journal = {KuVS Fachgespr{\"a}ch - W{\"u}rzburg Workshop on Modeling, Analysis and Simulation of Next-Generation Communication Networks 2023 (WueWoWAS'23)}, doi = {10.25972/OPUS-32202}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-322025}, pages = {4}, year = {2023}, abstract = {The emerging serverless computing may meet Edge Cloud in a beneficial manner as the two offer flexibility and dynamicity in optimizing finite hardware resources. However, the lack of proper study of a joint platform leaves a gap in literature about consumption and performance of such integration. To this end, this paper identifies the key questions and proposes a methodology to answer them.}, language = {en} } @article{HentschelKobsHotho2022, author = {Hentschel, Simon and Kobs, Konstantin and Hotho, Andreas}, title = {CLIP knows image aesthetics}, series = {Frontiers in Artificial Intelligence}, volume = {5}, journal = {Frontiers in Artificial Intelligence}, issn = {2624-8212}, doi = {10.3389/frai.2022.976235}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-297150}, year = {2022}, abstract = {Most Image Aesthetic Assessment (IAA) methods use a pretrained ImageNet classification model as a base to fine-tune. We hypothesize that content classification is not an optimal pretraining task for IAA, since the task discourages the extraction of features that are useful for IAA, e.g., composition, lighting, or style. On the other hand, we argue that the Contrastive Language-Image Pretraining (CLIP) model is a better base for IAA models, since it has been trained using natural language supervision. Due to the rich nature of language, CLIP needs to learn a broad range of image features that correlate with sentences describing the image content, composition, environments, and even subjective feelings about the image. While it has been shown that CLIP extracts features useful for content classification tasks, its suitability for tasks that require the extraction of style-based features like IAA has not yet been shown. We test our hypothesis by conducting a three-step study, investigating the usefulness of features extracted by CLIP compared to features obtained from the last layer of a comparable ImageNet classification model. In each step, we get more computationally expensive. First, we engineer natural language prompts that let CLIP assess an image's aesthetic without adjusting any weights in the model. To overcome the challenge that CLIP's prompting only is applicable to classification tasks, we propose a simple but effective strategy to convert multiple prompts to a continuous scalar as required when predicting an image's mean aesthetic score. Second, we train a linear regression on the AVA dataset using image features obtained by CLIP's image encoder. The resulting model outperforms a linear regression trained on features from an ImageNet classification model. It also shows competitive performance with fully fine-tuned networks based on ImageNet, while only training a single layer. Finally, by fine-tuning CLIP's image encoder on the AVA dataset, we show that CLIP only needs a fraction of training epochs to converge, while also performing better than a fine-tuned ImageNet model. Overall, our experiments suggest that CLIP is better suited as a base model for IAA methods than ImageNet pretrained networks.}, language = {en} } @techreport{LeGrossmannKrieger2022, type = {Working Paper}, author = {Le, Duy Thanh and Großmann, Marcel and Krieger, Udo R.}, title = {Cloudless Resource Monitoring in a Fog Computing System Enabled by an SDN/NFV Infrastructure}, series = {W{\"u}rzburg Workshop on Next-Generation Communication Networks (WueWoWas'22)}, journal = {W{\"u}rzburg Workshop on Next-Generation Communication Networks (WueWoWas'22)}, doi = {10.25972/OPUS-28072}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-280723}, pages = {4}, year = {2022}, abstract = {Today's advanced Internet-of-Things applications raise technical challenges on cloud, edge, and fog computing. The design of an efficient, virtualized, context-aware, self-configuring orchestration system of a fog computing system constitutes a major development effort within this very innovative area of research. In this paper we describe the architecture and relevant implementation aspects of a cloudless resource monitoring system interworking with an SDN/NFV infrastructure. It realizes the basic monitoring component of the fundamental MAPE-K principles employed in autonomic computing. Here we present the hierarchical layering and functionality within the underlying fog nodes to generate a working prototype of an intelligent, self-managed orchestrator for advanced IoT applications and services. The latter system has the capability to monitor automatically various performance aspects of the resource allocation among multiple hosts of a fog computing system interconnected by SDN.}, subject = {Datennetz}, language = {en} } @article{FreyGassenmaierHofmannetal.2020, author = {Frey, Anna and Gassenmaier, Tobias and Hofmann, Ulrich and Schmitt, Dominik and Fette, Georg and Marx, Almuth and Heterich, Sabine and Boivin-Jahns, Val{\´e}rie and Ertl, Georg and Bley, Thorsten and Frantz, Stefan and Jahns, Roland and St{\"o}rk, Stefan}, title = {Coagulation factor XIII activity predicts left ventricular remodelling after acute myocardial infarction}, series = {ESC Heart Failure}, volume = {7}, journal = {ESC Heart Failure}, number = {5}, doi = {10.1002/ehf2.12774}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-236013}, pages = {2354-2364}, year = {2020}, abstract = {Aims Acute myocardial infarction (MI) is the major cause of chronic heart failure. The activity of blood coagulation factor XIII (FXIIIa) plays an important role in rodents as a healing factor after MI, whereas its role in healing and remodelling processes in humans remains unclear. We prospectively evaluated the relevance of FXIIIa after acute MI as a potential early prognostic marker for adequate healing. Methods and results This monocentric prospective cohort study investigated cardiac remodelling in patients with ST-elevation MI and followed them up for 1 year. Serum FXIIIa was serially assessed during the first 9 days after MI and after 2, 6, and 12 months. Cardiac magnetic resonance imaging was performed within 4 days after MI (Scan 1), after 7 to 9 days (Scan 2), and after 12 months (Scan 3). The FXIII valine-to-leucine (V34L) single-nucleotide polymorphism rs5985 was genotyped. One hundred forty-six patients were investigated (mean age 58 ± 11 years, 13\% women). Median FXIIIa was 118 \% (quartiles, 102-132\%) and dropped to a trough on the second day after MI: 109\%(98-109\%; P < 0.001). FXIIIa recovered slowly over time, reaching the baseline level after 2 to 6 months and surpassed baseline levels only after 12 months: 124 \% (110-142\%). The development of FXIIIa after MI was independent of the genotype. FXIIIa on Day 2 was strongly and inversely associated with the relative size of MI in Scan 1 (Spearman's ρ = -0.31; P = 0.01) and Scan 3 (ρ = -0.39; P < 0.01) and positively associated with left ventricular ejection fraction: ρ = 0.32 (P < 0.01) and ρ = 0.24 (P = 0.04), respectively. Conclusions FXIII activity after MI is highly dynamic, exhibiting a significant decline in the early healing period, with reconstitution 6 months later. Depressed FXIIIa early after MI predicted a greater size of MI and lower left ventricular ejection fraction after 1 year. The clinical relevance of these findings awaits to be tested in a randomized trial.}, language = {en} } @article{BaumeisterStrifflerBrandtetal.2016, author = {Baumeister, Joachim and Striffler, Albrecht and Brandt, Marc and Neumann, Michael}, title = {Collaborative Decision Support and Documentation in Chemical Safety with KnowSEC}, series = {Journal of Cheminformatics}, volume = {8}, journal = {Journal of Cheminformatics}, number = {21}, doi = {10.1186/s13321-016-0132-8}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-146575}, year = {2016}, abstract = {To protect the health of human and environment, the European Union implemented the REACH regulation for chemical substances. REACH is an acronym for Registration, Evaluation, Authorization, and Restriction of Chemicals. Under REACH, the authorities have the task of assessing chemical substances, especially those that might pose a risk to human health or environment. The work under REACH is scientifically, technically and procedurally a complex and knowledge-intensive task that is jointly performed by the European Chemicals Agency and member state authorities in Europe. The assessment of substances under REACH conducted in the German Environment Agency is supported by the knowledge-based system KnowSEC, which is used for the screening, documentation, and decision support when working on chemical substances. The software KnowSEC integrates advanced semantic technologies and strong problem solving methods. It allows for the collaborative work on substances in the context of the European REACH regulation. We discuss the applied methods and process models and we report on experiences with the implementation and use of the system.}, language = {en} } @article{ScharnaglKempfSchilling2019, author = {Scharnagl, Julian and Kempf, Florian and Schilling, Klaus}, title = {Combining Distributed Consensus with Robust H-infinity-Control for Satellite Formation Flying}, series = {Electronics}, volume = {8}, journal = {Electronics}, number = {319}, doi = {10.3390/electronics8030319}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-228431}, pages = {1-27}, year = {2019}, abstract = {Control methods that guarantee stability in the presence of uncertainties are mandatory in space applications. Further, distributed control approaches are beneficial in terms of scalability and to achieve common goals, especially in multi-agent setups like formation control. This paper presents a combination of robust H-infinity control and distributed control using the consensus approach by deriving a distributed consensus-based generalized plant description that can be used in H-infinity synthesis. Special focus was set towards space applications, namely satellite formation flying. The presented results show the applicability of the developed distributed robust control method to a simple, though realistic space scenario, namely a spaceborne distributed telescope. By using this approach, an arbitrary number of satellites/agents can be controlled towards an arbitrary formation geometry. Because of the combination with robust H-infinity control, the presented method satisfies the high stability and robustness demands as found e.g., in space applications.}, language = {en} }