Fakultät für Informatik und Mathematik
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Institute
In summary, this cumulative dissertation investigates the application of the conjugate gradient method CG for the optimization of artificial neural networks (NNs) and compares this method with common first-order optimization methods, especially the stochastic gradient descent (SGD).
The presented research results show that CG can effectively optimize both small and very large networks. However, the default machine precision of 32 bits can lead to problems. The best results are only achieved in 64-bits computations. The research also emphasizes the importance of the initialization of the NNs’ trainable parameters and shows that an initialization using singular value decomposition (SVD) leads to drastically lower error values. Surprisingly, shallow but wide NNs, both in Transformer and CNN architectures, often perform better than their deeper counterparts. Overall, the research results recommend a re-evaluation of the previous preference for extremely deep NNs and emphasize the potential of CG as an optimization method.
Understanding of financial data has always been a point of interest for market participants to make better informed decisions. Recently, different cutting edge technologies have been addressed in the Financial Technology (FinTech) domain, including numeracy understanding, opinion mining and financial ocument processing.
In this thesis, we are interested in analyzing the arguments of financial experts with the goal of supporting investment decisions. Although various business studies confirm the crucial role of argumentation in financial communications, no work has addressed this problem as a computational argumentation task. In other words, the automatic analysis of arguments. In this regard, this thesis presents contributions in the three essential axes of theory, data, and evaluation to fill the gap between argument mining and financial text.
First, we propose a method for determining the structure of the arguments stated by company representatives during the public announcement of their quarterly results and future estimations through earnings conference calls. The proposed scheme is derived from argumentation theory at the micro-structure level of discourse. We further conducted the corresponding annotation study and published the first financial dataset annotated with arguments: FinArg.
Moreover, we investigate the question of evaluating the quality of arguments in this financial genre of text. To tackle this challenge, we suggest using two levels of quality metrics, considering both the Natural Language Processing (NLP) literature of argument quality assessment and the financial era peculiarities.
Hence, we have also enriched the FinArg data with our quality dimensions to produce the FinArgQuality dataset.
In terms of evaluation, we validate the principle of ensemble learning on the argument identification and argument unit classification tasks. We show that combining a traditional machine learning model along with a deep learning one, via an integration model (stacking), improves the overall performance, especially in small dataset settings.
In addition, despite the fact that argument mining is mainly a domain dependent task, to this date, the number of studies that tackle the generalization of argument mining models is still relatively small. Therefore, using our stacking approach and in comparison to the transfer learning model of DistilBert, we address and analyze three real-world scenarios concerning the model robustness over completely unseen domains and unseen topics.
Furthermore, with the aim of the automatic assessment of argument strength, we have investigated and compared different (refined) versions of Bert-based models that incorporate external knowledge in the decision layer. Consequently, our method outperforms the baseline model by 13 ± 2% in terms of F1-score through integrating Bert with encoded categorical features.
Beyond our theoretical and methodological proposals, our model of argument quality assessment, annotated corpora, and evaluation approaches are publicly available, and can serve as strong baselines for future work in both FinNLP and computational argumentation domains.
Hence, directly exploiting this thesis, we proposed to the community, a new task/challenge related to the analysis of financial arguments: FinArg-1, within the framework of the NTCIR-17 conference.
We also used our proposals to react to the Touché challenge at the CLEF 2021 conference. Our contribution was selected among the «Best of Labs».
A Comprehensive Comparison of Fuzzy Extractor Schemes Employing Different Error Correction Codes
(2023)
This thesis deals with fuzzy extractors, security primitives often used in conjunction with Physical Unclonable Functions (PUFs). A fuzzy extractor works in two stages: The generation phase and the reproduction phase. In the generation phase, an Error Correction Code (ECC) is used to compute redundant bits for a given PUF response, which are then stored as helper data, and a key is extracted from the response. Then, in the reproduction phase, another (possibly noisy) PUF response can be used in conjunction with this helper data to extract the original key.
It is clear that the performance of the fuzzy extractor is strongly dependent on the underlying ECC. Therefore, a comparison of ECCs in the context of fuzzy extractors is essential in order to make them as suitable as possible for a given situation. It is important to note that due to the plethora of various PUFs with different characteristics, it is very unrealistic to propose a single metric by which the suitability of a given ECC can be measured.
First, we give a brief introduction to the topic, followed by a detailed description of the background of the ECCs and fuzzy extractors studied. Then, we summarise related work and describe an implementation of the ECCs under consideration. Finally, we carry out the actual comparison of the ECCs and the thesis concludes with a summary of the results and suggestions for future work.
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.
In den vergangenen Jahrzehnten hat es unübersehbar zahlreiche Fortschritte im Bereich der IT-Sicherheitsforschung gegeben, etwa in den Bereichen Systemsicherheit und Kryptographie. Es ist jedoch genauso unübersehbar, dass IT-Sicherheitsprobleme im Alltag der Menschen fortbestehen. Mutmaßlich liegt dies an der Komplexität von Alltagssituationen, in denen Sicherheitsmechanismen und Gerätefunktionalität sowie deren Heterogenität in schwer antizipierbarer Weise mit menschlichem Verständnis und Alltagsgebrauch interagieren. Um die wissenschaftliche Forschung besser auf Menschen und deren IT-Sicherheitsbedürfnisse auszurichten, müssen wir daher den Alltag der Menschen besser verstehen. Das Verständnis von Alltag ist in der Informatik jedoch noch unterentwickelt. Dieser Beitrag möchte das Forschungsfeld “Sicherheit in der Digitalisierung des Alltags” definieren, um Forschenden die Gelegenheit zu geben, ihre Anstrengungen in diesem Bereich zu bündeln. Wir machen dabei Vorschläge einerseits zur inhaltlichen Eingrenzung der informatischen Forschung. Andererseits möchten wir durch die Einbeziehung von Forschungsmethoden aus der Ethnografie, die Erkenntnisse aus der durchaus subjektiven Beobachtung des “Alltags” vieler einzelner Individuen zieht, zur methodischen Weiterentwicklung interdisziplinärer Forschung in diesem Feld beitragen. Die IT- Sicherheitsforschung kann dann Bestehendes gezielt für eine richtige Alltagstauglichkeit optimieren und neue grundlegende Sicherheitsfunktionalitäten für die konkreten Herausforderungen im Alltag entwickeln.
Due to the increasing amount of distributed renewable energy generation and the emerging high demand at consumer connection points, e. g., electric vehicles, the power distribution grid will reach its capacity limit at peak load times if it is not expensively enhanced. Alternatively, smart flexibility management that controls user assets can help to better utilize the existing power grid infrastructure for example by sharing available grid capacity among connected electric vehicles or by disaggregating flexibility requests to hybrid photovoltaic battery energy storage systems in households. Besides maintaining an acceptable state of the power distribution grid, these smart grid applications also need to ensure a certain quality of service and provide fairness between the individual participants, both of which are not extensively discussed in the literature. This thesis investigates two smart grid applications, namely electric vehicle charging-as-a-service and flexibility-provision-as-a-service from distributed energy storage systems in private households.
The electric vehicle charging service allocation is modeled with distributed queuing-based allocation mechanisms which are compared to new probabilistic algorithms. Both integrate user constraints (arrival time, departure time, and energy required) to manage the quality of service and fairness. In the queuing-based allocation mechanisms, electric vehicle charging requests are packetized into logical charging current packets, representing the smallest controllable size of the charging process. These packets are queued at hierarchically distributed schedulers, which allocate the available charging capacity using the time and frequency division multiplexing technique known from the networking domain. This allows multiple electric vehicles to be charged simultaneously with variable charging currents. To achieve high quality of service and fairness among electric vehicle charging processes, dynamic weights are introduced into a weighted fair queuing scheduler that considers electric vehicle departure time and required energy for prioritization. The distributed probabilistic algorithms are inspired by medium access protocols from computer networking, such as binary exponential backoff, and control the quality of service and fairness by adjusting sampling windows and waiting periods based on user requirements.
The second smart grid application under investigation aims to provide flexibility provision-as-a-service that disaggregates power flexibility requests to distributed battery energy storage systems in private households. Commonly, the main purpose of stationary energy storage is to store energy from a local photovoltaic system for later use, e. g., for overnight charging of an electric vehicle. This is optimized locally by a home energy management system, which also allows the scheduling of external flexibility requests defined by the deviation from the optimal power profile at the grid connection point, for example, to perform peak shaving at the transformer. This thesis discusses a linear heuristic and a meta heuristic to disaggregate a flexibility request to the single participating energy management systems that are grouped into a flexibility pool. Thereby, the linear heuristic iteratively assigns portions of the power flexibility to the most appropriate energy management system for one time slot after another, minimizing the total flexibility cost or maximizing the probability of flexibility delivery. In addition, a multi-objective genetic algorithm is proposed that also takes into account power grid aspects, quality of service, and fairness among par-ticipating households. The genetic operators are tailored to the flexibility disaggregation search space, taking into account flexibility and energy management system constraints, and enable power-optimized buffering of fitness values.
Both smart grid applications are validated on a realistic power distribution grid with real driving patterns and energy profiles for photovoltaic generation and household consumption. The results of all proposed algorithms are analyzed with respect to a set of newly defined metrics on quality of service, fairness, efficiency, and utilization of the power distribution grid. One of the main findings is that none of the tested algorithms outperforms the others in all quality of service metrics, however, integration of user expectations improves the service quality compared to simpler approaches. Furthermore, smart grid control that incorporates users and their flexibility allows the integration of high-load applications such as electric vehicle charging and flexibility aggregation from distributed energy storage systems into the existing electricity distribution infrastructure. However, there is a trade-off between power grid aspects, e. g., grid losses and voltage values, and the quality of service provided. Whenever active user interaction is required, means of controlling the quality of service of users’ smart grid applications are necessary to ensure user satisfaction with the services provided.
In the constrained planarity setting, we ask whether a graph admits a crossing-free drawing that additionally satisfies a given set of constraints. These constraints are often derived from very natural problems; prominent examples are Level Planarity, where vertices have to lie on given horizontal lines indicating a hierarchy, Partially Embedded Planarity, where we extend a given drawing without modifying already-drawn parts, and Clustered Planarity, where we additionally draw the boundaries of clusters which recursively group the vertices in a crossing-free manner. In the last years, the family of constrained planarity problems received a lot of attention in the field of graph drawing. Efficient algorithms were discovered for many of them, while a few others turned out to be NP-complete. In contrast to the extensive theoretical considerations and the direct motivation by applications, only very few of the found algorithms have been implemented and evaluated in practice.
The goal of this thesis is to advance the research on both theoretical as well as practical aspects of constrained planarity. On the theoretical side, we consider two types of constrained planarity problems. The first type are problems that individually constrain the rotations of vertices, that is they restrict the counter-clockwise cyclic orders of the edges incident to vertices. We give a simple linear-time algorithm for the problem Partially Embedded Planarity, which also generalizes to further constrained planarity variants of this type.
The second type of constrained planarity problem concerns more involved planarity variants that come down to the question whether there are embeddings of one or multiple graphs such that the rotations of certain vertices are in sync in a certain way. Clustered Planarity and a variant of the Simultaneous Embedding with Fixed Edges Problem (Connected SEFE-2) are well-known problems of this type. Both are generalized by our Synchronized Planarity problem, for which we give a quadratic algorithm. Through reductions from various other problems, we provide a unified modelling framework for almost all known efficiently solvable constrained planarity variants that also directly provides a quadratic-time solution to all of them.
For both our algorithms, a key ingredient for reaching an efficient solution is the usage of the right data structure for the problem at hand. In this case, these data structures are the SPQR-tree and the PC-tree, which describe planar embedding possibilities from a global and a local perspective, respectively. More specifically, PC-trees can be used to locally describe the possible cyclic orders of edges around vertices in all planar embeddings of a graph. This makes it a key component for our algorithms, as it allows us to test planarity while also respecting further constraints, and to communicate constraints arising from the surrounding graph structure between vertices with synchronized rotation.
Bridging over to the practical side, we present the first correct implementation of PC-trees. We also describe further improvements, which allow us to outperform all implementations of alternative data structures (out of which we only found very few to be fully correct) by at least a factor of 4. We show that this yields a simple and competitive planarity test that can also yield an embedding to certify planarity. We also use our PC-tree implementation to implement our quadratic algorithm for solving Synchronized Planarity. Here, we show that our algorithm greatly outperforms previous attempts at solving related problems like Clustered Planarity in practice. We also engineer its running time and show how degrees of freedom in the theoretical algorithm can be leveraged to yield an up to tenfold speed-up in practice.
CNT-PUFs: highly robust and heat-tolerant carbon-nanotube-based physical unclonable functions
(2023)
In this work, we explored a highly robust and unique Physical Unclonable Function (PUF) based on the stochastic assembly of single-walled Carbon NanoTubes (CNTs) integrated within a wafer-level technology. Our work demonstrated that the proposed CNT-based PUFs are exceptionally robust with an average fractional intra-device Hamming distance well below 0.01 both at room temperature and under varying temperatures in the range from 23 °C to 120 °C. We attributed the excellent heat tolerance to comparatively low activation energies of less than 40 meV extracted from an Arrhenius plot. As the number of unstable bits in the examined implementation is extremely low, our devices allow for a lightweight and simple error correction, just by selecting stable cells, thereby diminishing the need for complex error correction. Through a significant number of tests, we demonstrated the capability of novel nanomaterial devices to serve as highly efficient hardware security primitives.
Sentences that present a complex linguistic structure act as a major stumbling block for Natural Language Processing (NLP) applications whose predictive quality deteriorates with sentence length and complexity. The task of Text Simplification (TS) may remedy this situation. It aims to modify sentences in order to make them easier to process, using a set of rewriting operations, such as reordering, deletion or splitting. These transformations are executed with the objective of converting the input into a simplified output, while preserving its main idea and keeping it grammatically sound. State-of-the-art syntactic TS approaches suffer from two major drawbacks: first, they follow a very conservative approach in that they tend to retain the input rather than transforming it, and second, they ignore the cohesive nature of texts, where context spread across clauses or sentences is needed to infer the true meaning of a statement. To address these problems, we present a discourse-aware TS framework that is able to split and rephrase complex English sentences within the semantic context in which they occur. By generating a fine-grained output with a simple canonical structure that is easy to analyze by downstream applications, we tackle the first issue. For this purpose, we decompose a source sentence into smaller units by using a linguistically grounded transformation stage. The result is a set of selfcontained propositions, with each of them presenting a minimal semantic unit. To address the second concern, we suggest not only to split the input into isolated sentences, but to also incorporate the semantic context in the form of hierarchical structures and semantic relationships between the split propositions. In that way, we generate a semantic hierarchy of minimal propositions that benefits downstream Open Information Extraction (IE) tasks. To function well, the TS approach that we propose requires syntactically well-formed input sentences. It targets generalpurpose texts in English, such as newswire or Wikipedia articles, which commonly contain a high proportion of complex assertions.
In a second step, we present a method that allows state-of-the-art Open IE systems to leverage the semantic hierarchy of simplified sentences created by our discourseaware TS approach in constructing a lightweight semantic representation of complex assertions in the form of semantically typed predicate-argument structures. In that way, important contextual information of the extracted relations is preserved that allows for a proper interpretation of the output. Thus, we address the problem of extracting incomplete, uninformative or incoherent relational tuples that is commonly to be observed in existing Open IE approaches. Moreover, assuming that shorter sentences with a more regular structure are easier to process, the extraction of relational tuples is facilitated, leading to a higher coverage and accuracy of the extracted relations when operating on the simplified sentences. Aside from taking advantage of the semantic hierarchy of minimal propositions in existing Open IE Abstract approaches, we also develop an Open IE reference system, Graphene. It implements a relation extraction pattern upon the simplified sentences.
The framework we propose is evaluated within our reference TS implementation DisSim. In a comparative analysis, we demonstrate that our approach outperforms the state of the art in structural TS both in an automatic and a manual analysis. It obtains the highest score on three simplification datasets from two different domains with regard to SAMSA (0.67, 0.57, 0.54), a recently proposed metric targeted at automatically measuring the syntactic complexity of sentences which highly correlates with human judgments on structural simplicity and grammaticality. These findings are supported by the ratings from the human evaluation, which indicate that our baseline implementation DisSim returns fine-grained simplified sentences that achieve a high level of syntactic correctness and largely preserve the meaning of the input. Furthermore, a comparative analysis with the annotations contained in the RST Discourse Treebank (RST-DT) reveals that we are able to capture the contextual hierarchy between the split sentences with a precision of approximately 90% and reach an average precision of almost 70% for the classification of the rhetorical relations that hold between them. Finally, an extrinsic evaluation shows that when applying our TS framework as a pre-processing step, the performance of state-ofthe-art Open IE systems can be improved by up to 32% in precision and 30% in recall of the extracted relational tuples.
Accordingly, we can conclude that our proposed discourse-aware TS approach succeeds in transforming sentences that present a complex linguistic structure into a sequence of simplified sentences that are to a large extent grammatically correct, represent atomic semantic units and preserve the meaning of the input. Moreover, the evaluation provides sufficient evidence that our framework is able to establish a semantic hierarchy between the split sentences, generating a fine-grained representation of complex assertions in the form of hierarchically ordered and semantically interconnected propositions. Finally, we demonstrate that state-of-the-art Open IE systems benefit from using our TS approach as a pre-processing step by increasing both the accuracy and coverage of the extracted relational tuples for the majority of the Open IE approaches under consideration. In addition, we outline that the semantic hierarchy of simplified sentences can be leveraged to enrich the output of existing Open IE systems with additional meta information, thus transforming the shallow semantic representation of state-of-the-art approaches into a canonical context-preserving representation of relational tuples.