@article{MuenchRutterStumpf2023, author = {M{\"u}nch, Miriam and Rutter, Ignaz and Stumpf, Peter}, title = {Partial and Simultaneous Transitive Orientations via Modular Decompositions}, series = {Algorithmica}, volume = {86}, journal = {Algorithmica}, number = {4}, publisher = {Springer Nature}, address = {Berlin}, doi = {10.1007/s00453-023-01188-y}, url = {http://nbn-resolving.de/urn:nbn:de:101:1-2024022914111908687000}, pages = {1263 -- 1292}, year = {2023}, abstract = {A natural generalization of the recognition problem for a geometric graph class is the problem of extending a representation of a subgraph to a representation of the whole graph. A related problem is to find representations for multiple input graphs that coin- cide on subgraphs shared by the input graphs. A common restriction is the sunflower case where the shared graph is the same for each pair of input graphs. These problems translate to the setting of comparability graphs where the representations correspond to transitive orientations of their edges. We use modular decompositions to improve the runtime for the orientation extension problem and the sunflower orientation problem to linear time. We apply these results to improve the runtime for the partial represen- tation problem and the sunflower case of the simultaneous representation problem for permutation graphs to linear time. We also give the first efficient algorithms for these problems on circular permutation graphs.}, language = {en} } @article{TrautschHerboldGrabowski2023, author = {Trautsch, Alexander and Herbold, Steffen and Grabowski, Jens}, title = {Are automated static analysis tools worth it? An investigation into relative warning density and external software quality on the example of Apache open source projects}, series = {Empirical Software Engineering}, volume = {28}, journal = {Empirical Software Engineering}, number = {3}, publisher = {Springer Nature}, address = {Berlin}, doi = {10.1007/s10664-023-10301-2}, url = {http://nbn-resolving.de/urn:nbn:de:101:1-2023091108203018898026}, pages = {1 -- 21}, year = {2023}, abstract = {Automated Static Analysis Tools (ASATs) are part of software development best practices. ASATs are able to warn developers about potential problems in the code. On the one hand, ASATs are based on best practices so there should be a noticeable effect on software quality. On the other hand, ASATs suffer from false positive warnings, which developers have to inspect and then ignore or mark as invalid. In this article, we ask whether ASATs have a measurable impact on external software quality, using the example of PMD for Java. We investigate the relationship between ASAT warnings emitted by PMD on defects per change and per file. Our case study includes data for the history of each file as well as the differences between changed files and the project in which they are contained. We investigate whether files that induce a defect have more static analysis warnings than the rest of the project. Moreover, we investigate the impact of two different sets of ASAT rules. We find that, bug inducing files contain less static analysis warnings than other files of the project at that point in time. However, this can be explained by the overall decreasing warning density. When compared with all other changes, we find a statistically significant difference in one metric for all rules and two metrics for a subset of rules. However, the effect size is negligible in all cases, showing that the actual difference in warning density between bug inducing changes and other changes is small at best.}, language = {en} } @article{FruehwirthProchno2023, author = {Fr{\"u}hwirth, Lorenz and Prochno, Joscha}, title = {H{\"o}lder's inequality and its reverse — a probabilistic point of view}, series = {Mathematische Nachrichten}, volume = {296}, journal = {Mathematische Nachrichten}, number = {12}, publisher = {Wiley}, address = {Hoboken}, doi = {10.1002/mana.202200411}, url = {http://nbn-resolving.de/urn:nbn:de:101:1-2023062315175042989607}, pages = {5493 -- 5512}, year = {2023}, abstract = {In this article, we take a probabilistic look at H{\"o}lder's inequality, considering the ratio of terms in the classical H{\"o}lder inequality for random vectors in ℝ𝑛. We prove a central limit theorem for this ratio, which then allows us to reverse the inequality up to a multiplicative constant with high probability. The models of randomness include the uniform distribution on 𝓁𝑛𝑝 balls and spheres. We also provide a Berry-Esseen-type result and prove a large and a moderate deviation principle for the suitably normalized H{\"o}lder ratio.}, language = {en} } @phdthesis{Hofstadler2024, author = {Hofstadler, Julian}, title = {Qualitative and quantitative convergence results for randomised integration methods}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:739-opus4-15196}, school = {Universit{\"a}t Passau}, pages = {III, 80 Seiten}, year = {2024}, abstract = {In this thesis different randomised integration methods based on either, randomised Quasi-Monte Carlo, or (adaptive) Markov chain Monte Carlo methods are studied. Depending on the underlying integration problem we show qualitative and quantitative results, which ensure the asymptotic correctness of an algorithm or provide explicit error bounds. The first problem we consider is Lebesgue integration in the unit cube. We prove that a class of structured randomised integration methods is consistent w.r.t. convergence in mean and probability for any integrable function. Under slightly stronger integrability conditions we show that one also has almost sure convergence for median modified methods. We demonstrate the applicability of our theoretical results by considering randomly shifted lattice rules, randomised (t,d)-sequences, Latin hypercube samples, and randomised Frolov points. Secondly, we study integration w.r.t. probability measures which are available only via their non-normalised density. In this context we investigate Markov chain Monte Carlo methods which satisfy a spectral gap condition and functions which do not need to have a finite second moment. We prove error bounds for the absolute mean error where the rate of convergence is optimal. Illustrative scenarios where our theory is applicable are the random walk Metropolis algorithm as well as slice samplers. Finally, we study so-called adaptive increasingly rare Markov chain Monte Carlo algorithms. Based on a simultaneous Wasserstein contraction assumption we estimate the mean squared error and also prove bounds which characterise the path-wise convergence of the estimator. To demonstrate the applicability of our results we consider a number of examples, among which are doubly intractable distributions.}, language = {en} } @phdthesis{Greifenstein2024, author = {Greifenstein, Luisa}, title = {Supporting Primary School Programming Education through Formative Feedback}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:739-opus4-15188}, school = {Universit{\"a}t Passau}, pages = {viii, 204 Seiten}, year = {2024}, abstract = {Children are increasingly surrounded by computer science aspects in their everyday life. Primary school education aims at empowering children to participate in and reflect on their environment. Therefore, computer science related contents such as programming are increasingly introduced into primary school curricula. However, this also involves challenges in particular for teachers, who need to familiarise themselves with the new curriculum. As a result, primary school teachers often struggle to help primary school children with their programming issues. Corrective feedback given during the learning process (i. e. formative feedback) can help by promoting cognitive factors such as content knowledge. This thesis therefore aims to support primary school programming education through formative feedback. In order to shed light on different perspectives, both teachers and children participated in the studies in a mixed methods design. Teachers' challenges and children's programming issues were explored as a basis for knowing what both target groups struggle with. This was done by conducting content analysis on the challenges and issues collected and using the resulting categories for further quantitative analysis. The effects of different characteristics of feedback on the effectiveness and efficiency of teaching and learning programming were then explained. This was done by asking the teachers and children to explain their ratings and conducting content analysis on their explanations. The support of primary school programming education through formative feedback builds on the major challenge of teachers' lack of content knowledge and the corresponding strategies of teacher training and automated feedback. Indeed, automated feedback was found to be mostly helpful for debugging and task creation. In order to provide direct support to children, common programming issues were identified in terms of the understanding of programming concepts and the usage of the programming environment. Effects on children's learning and their preferences were identified for several feedback characteristics, leading for example to elaborated hints instead of simple direct instructions. Based on these results, a formative feedback approach of hint cards was developed and evaluated. The hint cards approach proved to be a useful example strategy for supporting primary school programming education through formative feedback.}, subject = {R{\"u}ckmeldung}, language = {en} } @phdthesis{Welearegai2023, author = {Welearegai, Gebrehiwet Biyane}, title = {Precise Detection of Injection Attacks in Real-world Applications}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:739-opus4-12926}, school = {Universit{\"a}t Passau}, pages = {xvi, 120 Seiten}, year = {2023}, abstract = {Code injection attacks like the one used in the high-profile 2017 Equifax breach, have become increasingly common, ranking at the top of OWASP's list of critical web application vulnerabilities. The injection attacks can also target embedded applications running on processors like ARM and Xtensa by exploiting memory bugs and maliciously altering the program's behavior or even taking full control over a system. Especially, ARM's support of low power consumption without sacrificing performance is leading the industry to shift towards ARM processors, which advances the attention of injection attacks as well. In this thesis, we are considering web applications and embedded applications (running on ARM and Xtensa processors) as the target of injection attacks. To detect injection attacks in web applications, taint analysis is mostly proposed but the precision, scalability, and runtime overhead of the detection depend on the analysis types (e.g., static vs dynamic, sound vs unsound). Moreover, in the existing dynamic taint tracking approach for Java- based applications, even the most performant can impose a slowdown of at least 10-20\% and often far more. On the other hand, considering the embedded applications, while some initial research has tried to detect injection attacks (i.e., ROP and JOP) on ARM, they suffer from high performance or storage overhead. Besides, the Xtensa has been neglected though used in most firmware-based embedded WiFi home automation devices. This thesis aims to provide novel approaches to precisely detect injection attacks on both the web and embedded applications. To that end, we evaluate JavaScript static analysis frameworks to evaluate the security of a hybrid app (JS \& native) from an industrial partner, provide RIVULET - a tool that precisely detects injection attacks in Java-based real-world applications, and investigate injection attacks detection on ARM and Xtensa platforms using hardware performance counters (HPCs) and machine learning (ML) techniques. To evaluate the security of the hybrid application, we initially compare the precision, scalability, and code coverage of two widely-used static analysis frameworks—WALA and SAFE. The result of our comparison shows that SAFE provides higher precision and better code coverage at the cost of somewhat lower scalability. Based on these results, we analyze the data flows of the hybrid app via taint analysis by extending the SAFE's taint analysis and detected a potential for injection attacks of the hybrid application. Similarly, to detect injection attacks in Java-based applications, we provide Rivulet which monitors the execution of developer-written functional tests using dynamic taint tracking. Rivulet uses a white-box test generation technique to re-purpose those functional tests to check if any vulnerable flow could be exploited. We compared Rivulet to the state-of-the-art static vulnerability detector Julia on benchmarks and Rivulet outperformed Julia in both false positives and false negatives. We also used Rivulet to detect new vulnerabilities. Moreover, for applications running on ARM and Xtensa platforms, we investigate ROP1 attack detection by combining HPCs and ML techniques. We collect data exploiting real- world vulnerable applications and small benchmarks to train the ML. For ROP attack detection on ARM, we also implement an online monitor which labels a program's execution as benign or under attack and stops its execution once the latter is detected. Evaluating our ROP attack detection approach on ARM provides a detection accuracy of 92\% for the offline training and 75\% for the online monitoring. Similarly, our ROP attack detection on the firmware-only Xtensa processor provides an overall average detection accuracy of 79\%. Last but not least, this thesis shows how relevant taint analysis is to precisely detect injection attacks on web applications and the power of HPC combined with machine learning in the control flow injection attacks detection on ARM and Xtensa platforms.}, language = {en} } @article{GhoshDastidarJurgovskySiblinietal.2022, author = {Ghosh Dastidar, Kanishka and Jurgovsky, Johannes and Siblini, Wissam and Granitzer, Michael}, title = {NAG: neural feature aggregation framework for credit card fraud detection}, series = {Knowledge and Information Systems}, volume = {2022}, journal = {Knowledge and Information Systems}, number = {64}, publisher = {Springer Nature}, address = {Berlin}, doi = {10.1007/s10115-022-01653-0}, url = {http://nbn-resolving.de/urn:nbn:de:101:1-2022060219175235764329}, pages = {831 -- 858}, year = {2022}, abstract = {The state-of-the-art feature-engineering method for fraud classification of electronic pay-ments uses manually engineered feature aggregates, i.e., descriptive statistics of thetransaction history. However, this approach has limitations, primarily that of being dependenton expensive human expert knowledge. There have been attempts to replace manual aggre-gation through automatic feature extraction approaches. They, however, do not consider thespecific structure of the manual aggregates. In this paper, we define the novel Neural Aggre-gate Generator (NAG), a neural network-based feature extraction module that learns featureaggregates end-to-end on the fraud classification task. In contrast to other automatic featureextraction approaches, the network architecture of the NAG closely mimics the structureof feature aggregates. Furthermore, the NAG extends learnable aggregates over traditionalones through soft feature value matching and relative weighting of the importance of differ-ent feature constraints. We provide a proof to show the modeling capabilities of the NAG.We compare the performance of the NAG to the state-of-the-art approaches on a real-worlddataset with millions of transactions. More precisely, we show that features generated with theNAG lead to improved results over manual aggregates for fraud classification, thus demon-strating its viability to replace them. Moreover, we compare the NAG to other end-to-endapproaches such as the LSTM or a generic CNN. Here we also observe improved results. Weperform a robust evaluation of the NAG through a parameter budget study, an analysis of theimpact of different sequence lengths and also the predictions across days. Unlike the LSTMor the CNN, our approach also provides further interpretability through the inspection of itsparameters.}, language = {en} } @phdthesis{Patil2025, author = {Patil, Amit Dilip}, title = {Towards Resilient Protection of Interconnected ICT and Power Systems}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:739-opus4-16088}, school = {Universit{\"a}t Passau}, pages = {xxii, 173 Seiten}, year = {2025}, abstract = {Due to the increasing number of distributed renewable energy sources, the distribution grid faces new operational challenges. Information and Communication Technology (ICT) systems can resolve these challenges through grid services that use automation, monitoring, and real-time decision-making, helping maintain an acceptable operational state of the distribution grid. However, the reliance of the power system on the ICT system and vice versa in the so-called smart grid creates interdependencies between the systems, which present new pathways for failure propagation. Therefore, these interdependencies require special attention to ensure stable system operation in the face of these challenges. However, these interdependencies have not been studied extensively in the literature. This thesis investigates approaches to model, quantify and improve the performance and resilience of the smart grid infrastructure. The interdependencies are formalised as interconnectors, entities that exist in all the connected systems. These interconnectors consist of components from both systems, where the components are modelled as state variables. These state variables determine the interconnector state and the service delivered. Failures represented by a change in state variables may impact the state. These state variables are deployed in a discrete event simulation framework to determine the system performance over time. The simulation result is represented on a two-dimensional state-space diagram depicting the operational state and service delivered. This allows for the resilience analysis of a system under various scenarios. The interdependencies are further investigated by exploring the role of ICT-based grid services in power grids, whose state is defined based on ICT properties, such as latency. These properties are formalised using property graphs. The ICT properties obtained from these graphs are used to parameterise a finite state automaton model of a grid service states, which are then used to determine the state of the entire smart grid. Case studies of state estimation and adaptive protection highlight the application of this approach. The use of ICT in state estimation allows the distinction of a global and perceived view of the power grid, which influences decision-making in the face of challenges. This thesis further investigates the protection system in detail. The overcurrent protection system is adversely impacted by distributed generation, resulting in undesired phenomena such as protection blinding. This thesis characterises this phenomenon by proposing two indices that capture the protection trip time under the influence of distributed generation. These indices consider the electrical distance between faults, protection and distributed generation. These indices and simulation results identify the worst-impacted locations in the power grid in terms of protection trip time. They also identify fault locations under given assumptions that do not cause protection blinding. ICT can resolve protection blinding by adapting the sensitivity of protection relays. However, since faults must be cleared in a short timeframe, communication delays may adversely impact the fault-clearing time. A discrete event simulation model is proposed to study protection performance in distribution grids. Investigation of time distribution assumptions reveals that the lognormal distribution accurately captures the circuit breaker trip time. The impact of the distributed generation and communication delay on the protection system is determined by measuring fault-clearing times using discrete event simulation. Results show that for the system studied, protection blinding is critical for low impedance faults in grids with high fault levels, while high impedance faults are critical in grids with low fault levels. Moreover, sympathetic tripping is seen at increased distribution grid fault levels and fault impedance. Furthermore, while communication systems reduce fault clearing times, increased delays harm protection systems. Finally, communication system components like sensors can fail, preventing fault detection. This thesis proposes a genetic algorithm-based approach to optimally place redundant sensors, minimising protection blinding under communication uncertainty within a redundancy budget. Results demonstrate the algorithm's effectiveness in optimising redundant sensor locations, reducing system costs, and improving fault tolerance. For the system and scenarios investigated, an average of 60\% redundant sensors are relocated, reducing the average protection trip time by 36.65\% compared to a baseline approach that does not consider communication uncertainty. This encourages incorporating communication component failure considerations in power system planning.}, language = {en} } @article{FekihHassenChallouf2024, author = {Fekih Hassen, Wiem and Challouf, Maher}, title = {Long short-term renewable energy sources prediction for grid-management systems based on stacking ensemble model}, series = {Energies}, volume = {2024}, journal = {Energies}, number = {17(13)}, doi = {10.3390/en17133145}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:739-opus4-14649}, pages = {19 Seiten}, year = {2024}, abstract = {The transition towards sustainable energy systems necessitates effective management of renewable energy sources alongside conventional grid infrastructure. This paper presents a comprehensive approach to optimizing grid management by integrating Photovoltaic (PV), wind, and grid energies to minimize costs and enhance sustainability. A key focus lies in developing an accurate scheduling algorithm utilizing Mixed Integer Programming (MIP), enabling dynamic allocation of energy resources to meet demand while minimizing reliance on cost-intensive grid energy. An ensemble learning technique, specifically a stacking algorithm, is employed to construct a robust forecasting pipeline for PV and wind energy generation. The forecasting model achieves remarkable accuracy with a Root Mean Squared Error (RMSE) of less than 0.1 for short-term (15 min and one day ahead) and long-term (one week and one month ahead) predictions. By combining optimization and forecasting methodologies, this research contributes to advancing grid management systems capable of harnessing renewable energy sources efficiently, thus facilitating cost savings and fostering sustainability in the energy sector.}, language = {en} } @article{Kaiser2023, author = {Kaiser, Tobias}, title = {Growth of log-analytic functions}, series = {Archiv der Mathematik}, volume = {120}, journal = {Archiv der Mathematik}, number = {6}, publisher = {Springer Nature}, address = {Berlin}, doi = {10.1007/s00013-023-01857-y}, url = {http://nbn-resolving.de/urn:nbn:de:101:1-2023091614564557203657}, pages = {605 -- 614}, year = {2023}, abstract = {We show that unary log-analytic functions are polynomially bounded. In the higher dimensional case, globally a log-analytic function can have exponential growth. We show that a log-analytic function is polynomially bounded on a definable set which contains the germ of every ray at infinity.}, language = {en} } @article{Kaiser2024, author = {Kaiser, Tobias}, title = {Periods, power series, and integrated algebraic numbers}, series = {Mathematische Annalen}, volume = {390}, journal = {Mathematische Annalen}, number = {2}, publisher = {Springer Berlin Heidelberg}, address = {Berlin/Heidelberg}, issn = {0025-5831}, doi = {10.1007/s00208-024-02802-2}, url = {http://nbn-resolving.de/urn:nbn:de:101:1-2024040910285674698609}, pages = {2043 -- 2074}, year = {2024}, abstract = {Periods are defined as integrals of semialgebraic functions defined over the rationals. Periods form a countable ring not much is known about. Examples are given by taking the antiderivative of a power series which is algebraic over the polynomial ring over the rationals and evaluate it at a rational number. We follow this path and close these algebraic power series under taking iterated antiderivatives and nearby algebraic and geometric operations. We obtain a system of rings of power series whose coefficients form a countable real closed field. Using techniques from o-minimality we are able to show that every period belongs to this field. In the setting of o-minimality we define exponential integrated algebraic numbers and show that exponential periods and the Euler constant is an exponential integrated algebraic number. Hence they are a good candiate for a natural number system extending the period ring and containing important mathematical constants.}, language = {en} } @phdthesis{Liang2023, author = {Liang, Hanning}, title = {Deflectometric Measurement of the Topography of Reflecting Freeform Surfaces in Motion}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:739-opus4-11672}, school = {Universit{\"a}t Passau}, pages = {xiv, 157 Seiten}, year = {2023}, abstract = {Measuring the topography of specular surfaces with strong surface structures in motion was impossible before this research. A new method based on singleshot phase-measuring de ectometry (SSPMD) and combining different solution aspects has been presented.}, language = {en} } @article{BecherGerl2022, author = {Becher, Stefan and Gerl, Armin}, title = {ConTra Preference Language: Privacy Preference Unification via Privacy Interfaces}, series = {Sensors}, volume = {22}, journal = {Sensors}, number = {14}, editor = {Sarne, Giuseppe Maria Luigi and Ma, Jianhua and Rosaci, Domenico and Srivastava, Gautam}, publisher = {MDPI}, address = {Basel, Switzerland}, issn = {1424-8220}, doi = {10.3390/s22145428}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:739-opus4-11218}, pages = {18 Seiten}, year = {2022}, abstract = {After the enactment of the GDPR in 2018, many companies were forced to rethink their privacy management in order to comply with the new legal framework. These changes mostly affect the Controller to achieve GDPR-compliant privacy policies and management.However, measures to give users a better understanding of privacy, which is essential to generate legitimate interest in the Controller, are often skipped. We recommend addressing this issue by the usage of privacy preference languages, whereas users define rules regarding their preferences for privacy handling. In the literature, preference languages only work with their corresponding privacy language, which limits their applicability. In this paper, we propose the ConTra preference language, which we envision to support users during privacy policy negotiation while meeting current technical and legal requirements. Therefore, ConTra preferences are defined showing its expressiveness, extensibility, and applicability in resource-limited IoT scenarios. In addition, we introduce a generic approach which provides privacy language compatibility for unified preference matching.}, language = {en} } @phdthesis{Lachat2024, author = {Lachat, Paul}, title = {Detecting Inference Attacks Involving Sensor Data}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:739-opus4-14149}, school = {Universit{\"a}t Passau}, pages = {xiii, 141 Seiten}, year = {2024}, abstract = {The collection of personal information by organizations has become increasingly essential for social interactions. Nevertheless, according to the GDPR (General Data Protection Regulation), the organizations have to protect collected data. Access Control (AC) mechanisms are traditionally used to secure information systems against unauthorized access to sensitive data. The increased availability of personal sensor data, thanks to IoT-oriented applications, motivates new services to offer insights about individuals. Consequently, data mining algorithms have been proposed to infer personal insights from collected sensor data. Although they can be used for genuine purposes, attackers can leverage those outcomes, combining them with other type of data, and further breaching individuals' privacy. Thus, bypassing AC mechanisms thanks to such insights is a concrete problem. We propose an inference detection system based on the analysis of queries issued on a sensor database. The knowledge obtained through these queries, and the inference channels corresponding to the use of data mining algorithms on sensor data to infer individual information, are described using Raw sensor data based Inference ChannEl Model (RICE-M). The detection is carried out by RICE-M based inference detection System (RICE-Sy). RICE-Sy considers at the time of the query, the knowledge that a user obtains via a new query and has obtained via his query history, and determines whether this is sufficient to allow that user to operate a channel. Thus, privacy protection systems can take advantage of the inferences detected by RICE-Sy, taking into account individuals' information obtained by the attackers via a database of sensors, to further protect these individuals.}, language = {en} } @phdthesis{Schlenker2022, author = {Schlenker, Florian}, title = {Delaunay Configuration B-Splines}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:739-opus4-11225}, school = {Universit{\"a}t Passau}, pages = {xxiii, 223 Seiten}, year = {2022}, abstract = {The generalization of univariate splines to higher dimensions is not straightforward. There are different approaches, each with its own advantages and drawbacks. A promising approach using Delaunay configurations and simplex splines is due to Neamtu. After recalling fundamentals of univariate splines, simplex splines, and the wellknown, multivariate DMS-splines, we address Neamtu's DCB-splines. He defined two variants that we refer to as the nonpooled and the pooled approach, respectively. Regarding these spline spaces, we contribute the following results. We prove that, under suitable assumptions on the knot set, both variants exhibit the local finiteness property, i.e., these spline spaces are locally finite-dimensional and at each point only a finite number of basis candidate functions have a nonzero value. Additionally, we establish a criterion guaranteeing these properties within a compact region under mitigated assumptions. Moreover, we show that the knot insertion process known from univariate splines does not work for DCB-splines and reason why this behavior is inherent to these spline spaces. Furthermore, we provide a necessary criterion for the knot insertion property to hold true for a specific inserted knot. This criterion is also sufficient for bivariate, nonpooled DCB-splines of degrees zero and one. Numerical experiments suggest that the sufficiency also holds true for arbitrary spline degrees. Univariate functions can be approximated in terms of splines using the Schoenberg operator, where the approximation error decreases quadratically as the maximum distance between consecutive knots is reduced. We show that the Schoenberg operator can be defined analogously for both variants of DCB-splines with a similar error bound. Additionally, we provide a counterexample showing that the basis candidate functions of nonpooled DCB-splines are not necessarily linearly independent, contrary to earlier statements in the literature. In particular, this implies that the corresponding functions are not a basis for the space of nonpooled DCB-splines.}, subject = {Spline}, language = {en} } @phdthesis{Stier2024, author = {Stier, Julian}, title = {Structure of Artificial Neural Networks : Empirical Investigations}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:739-opus4-14968}, school = {Universit{\"a}t Passau}, pages = {xxvi, 350 Seiten}, year = {2024}, abstract = {Within one decade, Deep Learning overtook the dominating solution methods of countless problems of artificial intelligence. "Deep" refers to the deep architectures with operations in manifolds of which there are no immediate observations. For these deep architectures some kind of structure is pre-defined -- but what is this structure? With a formal definition for structures of neural networks, neural architecture search problems and solution methods can be formulated under a common framework. Both practical and theoretical questions arise from closing the gap between applied neural architecture search and learning theory. Does structure make a difference or can it be chosen arbitrarily? This work is concerned with deep structures of artificial neural networks and examines automatic construction methods under empirical principles to shed light on to the so called ``black-box models''. Our contributions include a formulation of graph-induced neural networks that is used to pose optimisation problems for neural architecture. We analyse structural properties for different neural network objectives such as correctness, robustness or energy consumption and discuss how structure affects them. Selected automation methods for neural architecture optimisation problems are discussed and empirically analysed. With the insights gained from formalising graph-induced neural networks, analysing structural properties and comparing the applicability of neural architecture search methods qualitatively and quantitatively we advance these methods in two ways. First, new predictive models are presented for replacing computationally expensive evaluation schemes, and second, new generative models for informed sampling during neural architecture search are analysed and discussed.}, language = {en} } @phdthesis{Juhos2024, author = {Juhos, Michael}, title = {Probabilistic and geometric aspects of classical and non-commutative lp-type spaces in high dimensions}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:739-opus4-14857}, school = {Universit{\"a}t Passau}, pages = {viii, 157 Seiten}, year = {2024}, abstract = {This cumulative dissertation contains selected contributions to the field of asymptotic geometric analysis and high-dimensional probability. It is divided into two chapters: Chapter 1 explains some of the necessary theoretical background. In Section 1.1 it first gives a very concise history of asymptotic geometric analysis in general and then of the objects under study in particular, setting out some cornerstones in the discovery of the functional-analytic, geometric, and probabilistic properties of the spaces under consideration. The next section (1.2) gives the precise definitions and very basic properties of the three lp-type spaces that play a role in the contributed articles: the classical lp-sequence spaces, the mixed-norm sequence spaces, and the Schatten-classes Sp, each in its infinite- and finite-dimensional version. Section 1.3 is dedicated to the interplay between geometry and probability, expounding the general idea, introducing a few of the common tools, and exemplifying these on two kinds of limit theorems: Schechtman-Schmuckenschl{\"a}ger-type results and Poincar{\´e}-Maxwell-Borel lemmas. The first chapter concludes with Section 1.4, addressing a small sample of open questions pertaining to the contributed articles which are not answered in said articles and may be the interest of future research. The entirety of Chapter 2 consists of the contributed articles.}, language = {en} } @phdthesis{Auer2024, author = {Auer, Michael}, title = {Improving Automated Android Test Generation}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:739-opus4-14955}, school = {Universit{\"a}t Passau}, pages = {x, 140 Seiten}, year = {2024}, abstract = {Mobile apps are nowadays the preferred means to accomplish ubiquitous tasks like messaging, e-commerce and even playing games. Often, there exist multiple apps for the same purpose, and it is the choice of the end user to pick an appropriate app. Apps that behave unexpected, e.g., crash frequently, are sooner or later replaced, which isundesirable for the companies developing such apps. Thus, it is essential to tests apps properly before they are released onto the market. However, testing manually is often not only too cost-intensive but also too time-consuming in the short development phase, thus an automated solution is preferred. Testing mobile apps automatically received increased attention in the last decade from primarily people in academia, and several testing techniques evolved. One technique that yielded promising results, especially in different domains, is search-based software testing in which a metaheuristic, e.g., a genetic algorithm, is applied to solve an optimisation problem, e.g., test generation. A main objective of test generation is to produce tests that reveal as many faults as possible. This in turn requires the generation of tests that deeply explore the tested app. The core metric to quantify how much code tests cover is the measurement of code coverage, which can be computed at different levels of granularity ranging from determining the fraction of covered activities to a very fine-grained measurement that calculates the percentage of covered lines. This coverage information is then often used to guide the search of the employed metaheuristic. However, current automated test generation approaches produce tests with a rather low code coverage. Thus, a substantial part of tested apps remains unexplored, which in turn misses revealing deeply residing faults. We identified three core issues that are directly related to the generation of low-coverage tests. First, the applicability of current test generators is often limited. This comprises the fact that current state-of-the-art code coverage tools are incapable of instrumenting a substantial number of apps and consequently, test generators cannot utilise detailed coverage information during exploration. In addition, test generators are often only equipped with a primitive set of actions that are insufficient to simulate system events and complex user inputs. Second, the test execution is extremely time-consuming. This includes among other things the overhead associated with executing individual actions, intermediate restart operations as well as fitness evaluations. Since search-based algorithms require a substantial number of test executions to play out their strengths, the slow test execution impedes the effectiveness of the search. Third, the guidance offered by search-based algorithms is often hampered by applying inadequate fitness functions or by using non-representation-specific variation operators. In this thesis we address the problem of low-coverage tests in the Android domain by proposing several enhancements for the three identified core issues. Concerning the applicability problem, we provide the implementation of a robust code coverage tool that is capable of measuring coverage at different levels of granularity and requires no access to the source code. We also propose to include actions that can simulate system events as well as complex user inputs. Regarding the performance issue, we suggest the integration of a surrogate model that is capable of predicting the outcome of individual actions or complete tests over time in order to reduce the overall test execution costs. With respect to the lack of guidance offered by traditional search-based algorithms, we suggest alternative search strategies. In the case of a deceptive fitness landscape, we propose using novelty search algorithms. Alternatively, we suggest utilising estimation of distribution algorithms that require no crossover or mutation perators to sample new tests. While all those enhancements had a positive impact on the Android test generation process, the individual empirical studies highlighted that further research is necessary to unleash the full power of the proposed search-based algorithms. In particular, exploring complex user interfaces meaningfully requires more attention whether by introducing additional actions or by extracting valuable hints to infer reasonable text inputs. In addition, the guidance offered by fitness functions is often limited because they are either designed too coarse at all or do not accurately reflect the search objectives.}, language = {en} } @article{LenglerOprisSudholt2024, author = {Lengler, Johannes and Opris, Andre and Sudholt, Dirk}, title = {Analysing Equilibrium States for Population Diversity}, series = {Algorithmica (ISSN: 1432-0541)}, volume = {86}, journal = {Algorithmica (ISSN: 1432-0541)}, number = {7}, publisher = {Springer US}, address = {New York}, issn = {0178-4617}, doi = {10.1007/s00453-024-01226-3}, url = {http://nbn-resolving.de/urn:nbn:de:101:1-2406282100292.409184699704}, pages = {2317 -- 2351}, year = {2024}, abstract = {Population diversity is crucial in evolutionary algorithms as it helps with global exploration and facilitates the use of crossover. Despite many runtime analyses showing advantages of population diversity, we have no clear picture of how diversity evolves over time. We study how the population diversity of (μ+1)algorithms, measured by the sum of pairwise Hamming distances, evolves in a fitness-neutral environment. We give an exact formula for the drift of population diversity and show that it is driven towards an equilibrium state. Moreover, we bound the expected time for getting close to the equilibrium state. We find that these dynamics, including the location of the equilibrium, are unaffected by surprisingly many algorithmic choices. All unbiased mutation operators with the same expected number of bit flips have the same effect on the expected diversity. Many crossover operators have no effect at all, including all binary unbiased, respectful operators. We review crossover operators from the literature and identify crossovers that are neutral towards the evolution of diversity and crossovers that are not.}, language = {en} } @article{SchulteLedelHerbold2024, author = {Schulte, Lukas and Ledel, Benjamin and Herbold, Steffen}, title = {Studying the explanations for the automated prediction of bug and non-bug issues using LIME and SHAP}, series = {Empirical Software Engineering (ISSN: 1573-7616)}, volume = {29}, journal = {Empirical Software Engineering (ISSN: 1573-7616)}, number = {4}, publisher = {Springer US}, address = {New York}, issn = {1382-3256}, doi = {10.1007/s10664-024-10469-1}, url = {http://nbn-resolving.de/urn:nbn:de:101:1-2409232103207.812648424894}, pages = {29 Seiten}, year = {2024}, abstract = {Context The identification of bugs within issues reported to an issue tracking system is crucial for triage. Machine learning models have shown promising results for this task. However, we have only limited knowledge of how such models identify bugs. Explainable AI methods like LIME and SHAP can be used to increase this knowledge. Objective We want to understand if explainable AI provides explanations that are reasonable to us as humans and align with our assumptions about the model's decision-making. We also want to know if the quality of predictions is correlated with the quality of explanations. Methods We conduct a study where we rate LIME and SHAP explanations based on their quality of explaining the outcome of an issue type prediction model. For this, we rate the quality of the explanations, i.e., if they align with our expectations and help us understand the underlying machine learning model. Results We found that both LIME and SHAP give reasonable explanations and that correct predictions are well explained. Further, we found that SHAP outperforms LIME due to a lower ambiguity and a higher contextuality that can be attributed to the ability of the deep SHAP variant to capture sentence fragments. Conclusion We conclude that the model finds explainable signals for both bugs and non-bugs. Also, we recommend that research dealing with the quality of explanations for classification tasks reports and investigates rater agreement, since the rating of explanations is highly subjective.}, language = {en} } @article{AistleitnerFruehwirthProchno2024, author = {Aistleitner, Christoph and Fr{\"u}hwirth, Lorenz and Prochno, Joscha}, title = {Diophantine conditions in the law of the iterated logarithm for lacunary systems}, series = {Probability Theory and Related Fields (ISSN: 1432-2064)}, volume = {192}, journal = {Probability Theory and Related Fields (ISSN: 1432-2064)}, number = {1}, publisher = {Springer}, address = {Berlin/Heidelberg}, issn = {0178-8051}, doi = {10.1007/s00440-024-01272-6}, url = {http://nbn-resolving.de/urn:nbn:de:101:1-2406190937284.886045166092}, pages = {545 -- 574}, year = {2024}, abstract = {It is a classical observation that lacunary function systems exhibit many properties which are typical for systems of independent random variables. However, it had already been observed by Erdős and Fortet in the 1950s that probability theory's limit theorems may fail for lacunary sums (sum f(n_k x)) if the sequence ((n_k)_{k ge 1}) has a strong arithmetic "structure". The presence of such structure can be assessed in terms of the number of solutions k, l of two-term linear Diophantine equations (an_k - bn_l = c). As the first author proved with Berkes in 2010, saving an (arbitrarily small) unbounded factor for the number of solutions of such equations compared to the trivial upper bound, rules out pathological situations as in the Erdős-Fortet example, and guarantees that (sum f(n_k x)) satisfies the central limit theorem (CLT) in a form which is in accordance with true independence. In contrast, as shown by the first author, for the law of the iterated logarithm (LIL) the Diophantine condition which suffices to ensure "truly independent" behavior requires saving this factor of logarithmic order. In the present paper we show that, rather surprisingly, saving such a logarithmic factor is actually the optimal condition in the LIL case. This result reveals the remarkable fact that the arithmetic condition required of ((n_k)_{k ge 1}) to ensure that (sum f(n_k x)) shows "truly random" behavior is a different one at the level of the CLT than it is at the level of the LIL: the LIL requires a stronger arithmetic condition than the CLT does.}, language = {en} }