Institut für Informatik und Computational Science
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This thesis presents an attempt to use source code synthesised from Coq formalisations of device drivers for existing (micro)kernel operating systems, with a particular focus on the Linux Kernel.
In the first part, the technical background and related work are described. The focus is here on the possible approaches to synthesising certified software with Coq, namely the extraction to functional languages using the Coq extraction plugin and the extraction to Clight code using the CertiCoq plugin. It is noted that the implementation of CertiCoq is verified, whereas this is not the case for the Coq extraction plugin. Consequently, there is a correctness guarantee for the generated Clight code which does not hold for the code being generated by the Coq extraction plugin. Furthermore, the differences between user space and kernel space software are discussed in relation to Linux device drivers. It is elaborated that it is not possible to generate working Linux kernel module components using the Coq extraction plugin without significant modifications. In contrast, it is possible to produce working user space drivers both with the Coq extraction plugin and CertiCoq. The subsequent parts describe the main contributions of the thesis.
In the second part, it is demonstrated how to extend the Coq extraction plugin to synthesise foreign function calls between the functional language OCaml and the imperative language C. This approach has the potential to improve the type-safety of user space drivers. Furthermore, it is shown that the code being synthesised by CertiCoq cannot be used in kernel space without modifications to the necessary runtime. Consequently, the necessary modifications to the runtimes of CertiCoq and VeriFFI are introduced, resulting in the runtimes becoming compatible components of a Linux kernel module. Furthermore, justifications for the transformations are provided and possible further extensions to both plugins and solutions to failing garbage collection calls in kernel space are discussed.
The third part presents a proof of concept device driver for the Linux Kernel. To achieve this, the event handler of the original PC Speaker driver is partially formalised in Coq. Furthermore, some relevant formal properties of the formalised functionality are discussed. Subsequently, a kernel module is defined, utilising the modified variants of CertiCoq and VeriFFI to compile a working device driver. It is furthermore shown that it is possible to compile the synthesised code with CompCert, thereby extending the guarantee of correctness to the assembly layer. This is followed by a performance evaluation that compares a naive formalisation of the PC speaker functionality with the original PC Speaker driver pointing out the weaknesses in the formalisation and possible improvements. The part closes with a summary of the results, their implications and open questions being raised.
The last part lists all used sources, separated into scientific literature, documentations or reference manuals and artifacts, i.e. source code.
BCH Codes mit kombinierter Korrektur und Erkennung In dieser Arbeit wird auf Grundlage des BCH Codes untersucht, wie eine Fehlerkorrektur mit einer Erkennung höherer Fehleranzahlen kombiniert werden kann. Mit dem Verfahren der 1-Bit Korrektur mit zusätzlicher Erkennung höherer Fehler wurde ein Ansatz entwickelt, welcher die Erkennung zusätzlicher Fehler durch das parallele Lösen einfacher Gleichungen der Form s_x = s_1^x durchführt. Die Anzahl dieser Gleichungen ist linear zu der Anzahl der zu überprüfenden höheren Fehler.
In dieser Arbeit wurde zusätzlich für bis zu 4-Bit Korrekturen mit zusätzlicher Erkennung höherer Fehler ein weiterer allgemeiner Ansatz vorgestellt. Dabei werden parallel für alle korrigierbaren Fehleranzahlen spekulative Fehlerkorrekturen durchgeführt. Aus den bestimmten Fehlerstellen werden spekulative Syndromkomponenten erzeugt, durch welche die Fehlerstellen bestätigt und höhere erkennbare Fehleranzahlen ausgeschlossen werden können. Die vorgestellten Ansätze unterscheiden sich von dem in entwickelten Ansatz, bei welchem die Anzahl der Fehlerstellen durch die Berechnung von Determinanten in absteigender Reihenfolge berechnet wird, bis die erste Determinante 0 bildet. Bei dem bekannten Verfahren ist durch die Berechnung der Determinanten eine faktorielle Anzahl an Berechnungen in Relation zu der Anzahl zu überprüfender Fehler durchzuführen. Im Vergleich zu dem bekannten sequentiellen Verfahrens nach Berlekamp Massey besitzen die Berechnungen im vorgestellten Ansatz simple Gleichungen und können parallel durchgeführt werden.Bei dem bekannten Verfahren zur parallelen Korrektur von 4-Bit Fehlern ist eine Gleichung vierten Grades im GF(2^m) zu lösen. Dies erfolgt, indem eine Hilfsgleichung dritten Grades und vier Gleichungen zweiten Grades parallel gelöst werden. In der vorliegenden Arbeit wurde gezeigt, dass sich eine Gleichung zweiten Grades einsparen lässt, wodurch sich eine Vereinfachung der Hardware bei einer parallelen Realisierung der 4-Bit Korrektur ergibt. Die erzielten Ergebnisse wurden durch umfangreiche Simulationen in Software und Hardwareimplementierungen überprüft.
Due to anthropogenic greenhouse gas emissions, Earth’s average surface temperature is steadily increasing. As a consequence, many weather extremes are likely to become more frequent and intense. This poses a threat to natural and human systems, with local impacts capable of destroying exposed assets and infrastructure, and disrupting economic and societal activity. Yet, these effects are not locally confined to the directly affected regions, as they can trigger indirect economic repercussions through loss propagation along supply chains. As a result, local extremes yield a potentially global economic response. To build economic resilience and design effective adaptation measures that mitigate adverse socio-economic impacts of ongoing climate change, it is crucial to gain a comprehensive understanding of indirect impacts and the underlying economic mechanisms.
Presenting six articles in this thesis, I contribute towards this understanding. To this end, I expand on local impacts under current and future climate, the resulting global economic response, as well as the methods and tools to analyze this response.
Starting with a traditional assessment of weather extremes under climate change, the first article investigates extreme snowfall in the Northern Hemisphere until the end of the century. Analyzing an ensemble of global climate model projections reveals an increase of the most extreme snowfall, while mean snowfall decreases.
Assessing repercussions beyond local impacts, I employ numerical simulations to compute indirect economic effects from weather extremes with the numerical agent-based shock propagation model Acclimate. This model is used in conjunction with the recently emerged storyline framework, which involves analyzing the impacts of a particular reference extreme event and comparing them to impacts in plausible counterfactual scenarios under various climate or socio-economic conditions. Using this approach, I introduce three primary storylines that shed light on the complex mechanisms underlying economic loss propagation.
In the second and third articles of this thesis, I analyze storylines for the historical Hurricanes Sandy (2012) and Harvey (2017) in the USA. For this, I first estimate local economic output losses and then simulate the resulting global economic response with Acclimate. The storyline for Hurricane Sandy thereby focuses on global consumption price anomalies and the resulting changes in consumption. I find that the local economic disruption leads to a global wave-like economic price ripple, with upstream effects propagating in the supplier direction and downstream effects in the buyer direction. Initially, an upstream demand reduction causes consumption price decreases, followed by a downstream supply shortage and increasing prices, before the anomalies decay in a normalization phase. A dominant upstream or downstream effect leads to net consumption gains or losses of a region, respectively. Moreover, I demonstrate that a longer direct economic shock intensifies the downstream effect for many regions, leading to an overall consumption loss.
The third article of my thesis builds upon the developed loss estimation method by incorporating projections to future global warming levels. I use these projections to explore how the global production response to Hurricane Harvey would change under further increased global warming. The results show that, while the USA is able to nationally offset direct losses in the reference configuration, other countries have to compensate for increasing shares of counterfactual future losses. This compensation is mainly achieved by large exporting countries, but gradually shifts towards smaller regions. These findings not only highlight the economy’s ability to flexibly mitigate disaster losses to a certain extent, but also reveal the vulnerability and economic disadvantage of regions that are exposed to extreme weather events.
The storyline in the fourth article of my thesis investigates the interaction between global economic stress and the propagation of losses from weather extremes. I examine indirect impacts of weather extremes — tropical cyclones, heat stress, and river floods — worldwide under two different economic conditions: an unstressed economy and a globally stressed economy, as seen during the Covid-19 pandemic. I demonstrate that the adverse effects of weather extremes on global consumption are strongly amplified when the economy is under stress. Specifically, consumption losses in the USA and China double and triple, respectively, due to the global economy’s decreased capacity for disaster loss compensation. An aggravated scarcity intensifies the price response, causing consumption losses to increase.
Advancing on the methods and tools used here, the final two articles in my thesis extend the agent-based model Acclimate and formalize the storyline approach. With the model extension described in the fifth article, regional consumers make rational choices on the goods bought such that their utility is maximized under a constrained budget. In an out-of-equilibrium economy, these rational consumers are shown to temporarily increase consumption of certain goods in spite of rising prices.
The sixth article of my thesis proposes a formalization of the storyline framework, drawing on multiple studies including storylines presented in this thesis. The proposed guideline defines eight central elements that can be used to construct a storyline.
Overall, this thesis contributes towards a better understanding of economic repercussions of weather extremes. It achieves this by providing assessments of local direct impacts, highlighting mechanisms and impacts of loss propagation, and advancing on methods and tools used.
Reliable and robust data processing is one of the hardest requirements for systems in fields such as medicine, security, automotive, aviation, and space, to prevent critical system failures caused by changes in operating or environmental conditions. In particular, Signal Integrity (SI) effects such as crosstalk may distort the signal information in sensitive mixed-signal designs. A challenge for hardware systems used in the space are radiation effects. Namely, Single Event Effects (SEEs) induced by high-energy particle hits may lead to faulty computation, corrupted configuration settings, undesired system behavior, or even total malfunction.
Since these applications require an extra effort in design and implementation, it is beneficial to master the standard cell design process and corresponding design flow methodologies optimized for such challenges. Especially for reliable, low-noise differential signaling logic such as Current Mode Logic (CML), a digital design flow is an orthogonal approach compared to traditional manual design. As a consequence, mandatory preliminary considerations need to be addressed in more detail. First of all, standard cell library concepts with suitable cell extensions for reliable systems and robust space applications have to be elaborated. Resulting design concepts at the cell level should enable the logical synthesis for differential logic design or improve the radiation-hardness. In parallel, the main objectives of the proposed cell architectures are to reduce the occupied area, power, and delay overhead. Second, a special setup for standard cell characterization is additionally required for a proper and accurate logic gate modeling. Last but not least, design methodologies for mandatory design flow stages such as logic synthesis and place and route need to be developed for the respective hardware systems to keep the reliability or the radiation-hardness at an acceptable level.
This Thesis proposes and investigates standard cell-based design methodologies and techniques for reliable and robust hardware systems implemented in a conventional semi-conductor technology. The focus of this work is on reliable differential logic design and robust radiation-hardening-by-design circuits. The synergistic connections of the digital design flow stages are systematically addressed for these two types of hardware systems. In more detail, a library for differential logic is extended with single-ended pseudo-gates for intermediate design steps to support the logic synthesis and layout generation with commercial Computer-Aided Design (CAD) tools. Special cell layouts are proposed to relax signal routing. A library set for space applications is similarly extended by novel Radiation-Hardening-by-Design (RHBD) Triple Modular Redundancy (TMR) cells, enabling a one fault correction. Therein, additional optimized architectures for glitch filter cells, robust scannable and self-correcting flip-flops, and clock-gates are proposed. The circuit concepts and the physical layout representation views of the differential logic gates and the RHBD cells are discussed. However, the quality of results of designs depends implicitly on the accuracy of the standard cell characterization which is examined for both types therefore. The entire design flow is elaborated from the hardware design description to the layout representations. A 2-Phase routing approach together with an intermediate design conversion step is proposed after the initial place and route stage for reliable, pure differential designs, whereas a special constraining for RHBD applications in a standard technology is presented.
The digital design flow for differential logic design is successfully demonstrated on a reliable differential bipolar CML application. A balanced routing result of its differential signal pairs is obtained by the proposed 2-Phase-routing approach. Moreover, the elaborated standard cell concepts and design methodology for RHBD circuits are applied to the digital part of a 7.5-15.5 MSPS 14-bit Analog-to-Digital Converter (ADC) and a complex microcontroller architecture. The ADC is implemented in an unhardened standard semiconductor technology and successfully verified by electrical measurements. The overhead of the proposed hardening approach is additionally evaluated by design exploration of the microcontroller application. Furthermore, the first obtained related measurement results of novel RHBD-∆TMR flip-flops show a radiation-tolerance up to a threshold Linear Energy Transfer (LET) of 46.1, 52.0, and 62.5 MeV cm2 mg-1 and savings in silicon area of 25-50 % for selected TMR standard cell candidates.
As a conclusion, the presented design concepts at the cell and library levels, as well as the design flow modifications are adaptable and transferable to other technology nodes. In particular, the design of hybrid solutions with integrated reliable differential logic modules together with robust radiation-tolerant circuit parts is enabled by the standard cell concepts and design methods proposed in this work.
Accurately solving classification problems nowadays is likely to be the most relevant machine learning task. Binary classification separating two classes only is algorithmically simpler but has fewer potential applications as many real-world problems are multi-class. On the reverse, separating only a subset of classes simplifies the classification task. Even though existing multi-class machine learning algorithms are very flexible regarding the number of classes, they assume that the target set Y is fixed and cannot be restricted once the training is finished. On the other hand, existing state-of-the-art production environments are becoming increasingly interconnected with the advance of Industry 4.0 and related technologies such that additional information can simplify the respective classification problems. In light of this, the main aim of this thesis is to introduce dynamic classification that generalizes multi-class classification such that the target class set can be restricted arbitrarily to a non-empty class subset M of Y at any time between two consecutive predictions.
This task is solved by a combination of two algorithmic approaches. First, classifier calibration, which transforms predictions into posterior probability estimates that are intended to be well calibrated. The analysis provided focuses on monotonic calibration and in particular corrects wrong statements that appeared in the literature. It also reveals that bin-based evaluation metrics, which became popular in recent years, are unjustified and should not be used at all. Next, the validity of Platt scaling, which is the most relevant parametric calibration approach, is analyzed in depth. In particular, its optimality for classifier predictions distributed according to four different families of probability distributions as well its equivalence with Beta calibration up to a sigmoidal preprocessing are proven. For non-monotonic calibration, extended variants on kernel density estimation and the ensemble method EKDE are introduced. Finally, the calibration techniques are evaluated using a simulation study with complete information as well as on a selection of 46 real-world data sets.
Building on this, classifier calibration is applied as part of decomposition-based classification that aims to reduce multi-class problems to simpler (usually binary) prediction tasks. For the involved fusing step performed at prediction time, a new approach based on evidence theory is presented that uses classifier calibration to model mass functions. This allows the analysis of decomposition-based classification against a strictly formal background and to prove closed-form equations for the overall combinations. Furthermore, the same formalism leads to a consistent integration of dynamic class information, yielding a theoretically justified and computationally tractable dynamic classification model. The insights gained from this modeling are combined with pairwise coupling, which is one of the most relevant reduction-based classification approaches, such that all individual predictions are combined with a weight. This not only generalizes existing works on pairwise coupling but also enables the integration of dynamic class information.
Lastly, a thorough empirical study is performed that compares all newly introduced approaches to existing state-of-the-art techniques. For this, evaluation metrics for dynamic classification are introduced that depend on corresponding sampling strategies. Thereafter, these are applied during a three-part evaluation. First, support vector machines and random forests are applied on 26 data sets from the UCI Machine Learning Repository. Second, two state-of-the-art deep neural networks are evaluated on five benchmark data sets from a relatively recent reference work. Here, computationally feasible strategies to apply the presented algorithms in combination with large-scale models are particularly relevant because a naive application is computationally intractable. Finally, reference data from a real-world process allowing the inclusion of dynamic class information are collected and evaluated. The results show that in combination with support vector machines and random forests, pairwise coupling approaches yield the best results, while in combination with deep neural networks, differences between the different approaches are mostly small to negligible. Most importantly, all results empirically confirm that dynamic classification succeeds in improving the respective prediction accuracies. Therefore, it is crucial to pass dynamic class information in respective applications, which requires an appropriate digital infrastructure.
As a result of CMOS scaling, radiation-induced Single-Event Effects (SEEs) in electronic circuits became a critical reliability issue for modern Integrated Circuits (ICs) operating under harsh radiation conditions. SEEs can be triggered in combinational or sequential logic by the impact of high-energy particles, leading to destructive or non-destructive faults, resulting in data corruption or even system failure. Typically, the SEE mitigation methods are deployed statically in processing architectures based on the worst-case radiation conditions, which is most of the time unnecessary and results in a resource overhead. Moreover, the space radiation conditions are dynamically changing, especially during Solar Particle Events (SPEs). The intensity of space radiation can differ over five orders of magnitude within a few hours or days, resulting in several orders of magnitude fault probability variation in ICs during SPEs. This thesis introduces a comprehensive approach for designing a self-adaptive fault resilient multiprocessing system to overcome the static mitigation overhead issue. This work mainly addresses the following topics: (1) Design of on-chip radiation particle monitor for real-time radiation environment detection, (2) Investigation of space environment predictor, as support for solar particle events forecast, (3) Dynamic mode configuration in the resilient multiprocessing system. Therefore, according to detected and predicted in-flight space radiation conditions, the target system can be configured to use no mitigation or low-overhead mitigation during non-critical periods of time. The redundant resources can be used to improve system performance or save power. On the other hand, during increased radiation activity periods, such as SPEs, the mitigation methods can be dynamically configured appropriately depending on the real-time space radiation environment, resulting in higher system reliability. Thus, a dynamic trade-off in the target system between reliability, performance and power consumption in real-time can be achieved. All results of this work are evaluated in a highly reliable quad-core multiprocessing system that allows the self-adaptive setting of optimal radiation mitigation mechanisms during run-time. Proposed methods can serve as a basis for establishing a comprehensive self-adaptive resilient system design process. Successful implementation of the proposed design in the quad-core multiprocessor shows its application perspective also in the other designs.
Answer Set Programming (ASP) allows us to address knowledge-intensive search and optimization problems in a declarative way due to its integrated modeling, grounding, and solving workflow. A problem is modeled using a rule based language and then grounded and solved. Solving results in a set of stable models that correspond to solutions of the modeled problem. In this thesis, we present the design and implementation of the clingo system---perhaps, the most
widely used ASP system. It features a rich modeling language originating from the field of knowledge representation and reasoning, efficient grounding algorithms based on database evaluation techniques, and high performance solving algorithms based on Boolean satisfiability (SAT) solving technology.
The contributions of this thesis lie in the design of the modeling language, the design and implementation of the grounding algorithms, and the design and implementation of an Application Programmable Interface (API) facilitating the use of ASP in real world applications and the implementation of complex forms of reasoning beyond the traditional ASP workflow.
Das Promotionsvorhaben verfolgt das Ziel, die Zuverlässigkeit der Datenspeicherung und die Speicherdichte von neu entwickelten Speichern (Emerging Memories) mit Multi-Level-Speicherzellen zu verbessern bzw. zu erhöhen. Hierfür werden Codes zur Erkennung von unidirektionalen Fehlern analysiert, modifiziert und neu entwickelt, um sie innerhalb der neuen Speicher anwenden zu können. Der Fokus liegt dabei auf sog. Berger-Codes und m-aus-n-Codes. Da Multi-Level-Speicherzellen nicht mehr binär, sondern mit mehreren Leveln arbeiten, können bisher verwendete Codes nicht mehr verwendet werden, bzw. müssen entsprechend angepasst werden. Auf Basis der Berger-Codes und m-aus-n-Codes werden in dieser Arbeit neue Codes abgeleitet, welche in der Lage sind, Daten auch in mehrwertigen Systemen zu schützen.
With the downscaling of CMOS technologies, the radiation-induced Single Event Transient (SET) effects in combinational logic have become a critical reliability issue for modern integrated circuits (ICs) intended for operation under harsh radiation conditions. The SET pulses generated in combinational logic may propagate through the circuit and eventually result in soft errors. It has thus become an imperative to address the SET effects in the early phases of the radiation-hard IC design. In general, the soft error mitigation solutions should accommodate both static and dynamic measures to ensure the optimal utilization of available resources. An efficient soft-error-aware design should address synergistically three main aspects: (i) characterization and modeling of soft errors, (ii) multi-level soft error mitigation, and (iii) online soft error monitoring. Although significant results have been achieved, the effectiveness of SET characterization methods, accuracy of predictive SET models, and efficiency of SET mitigation measures are still critical issues. Therefore, this work addresses the following topics: (i) Characterization and modeling of SET effects in standard combinational cells, (ii) Static mitigation of SET effects in standard combinational cells, and (iii) Online particle detection, as a support for dynamic soft error mitigation.
Since the standard digital libraries are widely used in the design of radiation-hard ICs, the characterization of SET effects in standard cells and the availability of accurate SET models for the Soft Error Rate (SER) evaluation are the main prerequisites for efficient radiation-hard design. This work introduces an approach for the SPICE-based standard cell characterization with the reduced number of simulations, improved SET models and optimized SET sensitivity database. It has been shown that the inherent similarities in the SET response of logic cells for different input levels can be utilized to reduce the number of required simulations. Based on characterization results, the fitting models for the SET sensitivity metrics (critical charge, generated SET pulse width and propagated SET pulse width) have been developed. The proposed models are based on the principle of superposition, and they express explicitly the dependence of the SET sensitivity of individual combinational cells on design, operating and irradiation parameters. In contrast to the state-of-the-art characterization methodologies which employ extensive look-up tables (LUTs) for storing the simulation results, this work proposes the use of LUTs for storing the fitting coefficients of the SET sensitivity models derived from the characterization results. In that way the amount of characterization data in the SET sensitivity database is reduced significantly.
The initial step in enhancing the robustness of combinational logic is the application of gate-level mitigation techniques. As a result, significant improvement of the overall SER can be achieved with minimum area, delay and power overheads. For the SET mitigation in standard cells, it is essential to employ the techniques that do not require modifying the cell structure. This work introduces the use of decoupling cells for improving the robustness of standard combinational cells. By insertion of two decoupling cells at the output of a target cell, the critical charge of the cell’s output node is increased and the attenuation of short SETs is enhanced. In comparison to the most common gate-level techniques (gate upsizing and gate duplication), the proposed approach provides better SET filtering. However, as there is no single gate-level mitigation technique with optimal performance, a combination of multiple techniques is required. This work introduces a comprehensive characterization of gate-level mitigation techniques aimed to quantify their impact on the SET robustness improvement, as well as introduced area, delay and power overhead per gate. By characterizing the gate-level mitigation techniques together with the standard cells, the required effort in subsequent SER analysis of a target design can be reduced. The characterization database of the hardened standard cells can be utilized as a guideline for selection of the most appropriate mitigation solution for a given design.
As a support for dynamic soft error mitigation techniques, it is important to enable the online detection of energetic particles causing the soft errors. This allows activating the power-greedy fault-tolerant configurations based on N-modular redundancy only at the high radiation levels. To enable such a functionality, it is necessary to monitor both the particle flux and the variation of particle LET, as these two parameters contribute significantly to the system SER. In this work, a particle detection approach based on custom-sized pulse stretching inverters is proposed. Employing the pulse stretching inverters connected in parallel enables to measure the particle flux in terms of the number of detected SETs, while the particle LET variations can be estimated from the distribution of SET pulse widths. This approach requires a purely digital processing logic, in contrast to the standard detectors which require complex mixed-signal processing. Besides the possibility of LET monitoring, additional advantages of the proposed particle detector are low detection latency and power consumption, and immunity to error accumulation.
The results achieved in this thesis can serve as a basis for establishment of an overall soft-error-aware database for a given digital library, and a comprehensive multi-level radiation-hard design flow that can be implemented with the standard IC design tools. The following step will be to evaluate the achieved results with the irradiation experiments.
Die stetige Weiterentwicklung von VR-Systemen bietet neue Möglichkeiten der Interaktion mit virtuellen Objekten im dreidimensionalen Raum, stellt Entwickelnde von VRAnwendungen aber auch vor neue Herausforderungen. Selektions- und Manipulationstechniken müssen unter Berücksichtigung des Anwendungsszenarios, der Zielgruppe und der zur Verfügung stehenden Ein- und Ausgabegeräte ausgewählt werden. Diese Arbeit leistet einen Beitrag dazu, die Auswahl von passenden Interaktionstechniken zu unterstützen. Hierfür wurde eine repräsentative Menge von Selektions- und Manipulationstechniken untersucht und, unter Berücksichtigung existierender Klassifikationssysteme, eine Taxonomie entwickelt, die die Analyse der Techniken hinsichtlich interaktionsrelevanter Eigenschaften ermöglicht. Auf Basis dieser Taxonomie wurden Techniken ausgewählt, die in einer explorativen Studie verglichen wurden, um Rückschlüsse auf die Dimensionen der Taxonomie zu ziehen und neue Indizien für Vor- und Nachteile der Techniken in spezifischen Anwendungsszenarien zu generieren. Die Ergebnisse der Arbeit münden in eine Webanwendung, die Entwickelnde von VR-Anwendungen gezielt dabei unterstützt, passende Selektions- und Manipulationstechniken für ein Anwendungsszenario auszuwählen, indem Techniken auf Basis der Taxonomie gefiltert und unter Verwendung der Resultate aus der Studie sortiert werden können.
The Internet of Things (IoT) is a system of physical objects that can be discovered, monitored, controlled, or interacted with by electronic devices that communicate over various networking interfaces and eventually can be connected to the wider Internet. [Guinard and Trifa, 2016]. IoT devices are equipped with sensors and/or actuators and may be constrained in terms of memory, computational power, network bandwidth, and energy. Interoperability can help to manage such heterogeneous devices. Interoperability is the ability of different types of systems to work together smoothly. There are four levels of interoperability: physical, network and transport, integration, and data. The data interoperability is subdivided into syntactic and semantic data. Semantic data describes the meaning of data and the common understanding of vocabulary e.g. with the help of dictionaries, taxonomies, ontologies. To achieve interoperability, semantic interoperability is necessary.
Many organizations and companies are working on standards and solutions for interoperability in the IoT. However, the commercial solutions produce a vendor lock-in. They focus on centralized approaches such as cloud-based solutions. This thesis proposes a decentralized approach namely Edge Computing. Edge Computing is based on the concepts of mesh networking and distributed processing. This approach has an advantage that information collection and processing are placed closer to the sources of this information. The goals are to reduce traffic, latency, and to be robust against a lossy or failed Internet connection.
We see management of IoT devices from the network configuration management perspective. This thesis proposes a framework for network configuration management of heterogeneous, constrained IoT devices by using semantic descriptions for interoperability. The MYNO framework is an acronym for MQTT, YANG, NETCONF and Ontology. The NETCONF protocol is the IETF standard for network configuration management. The MQTT protocol is the de-facto standard in the IoT. We picked up the idea of the NETCONF-MQTT bridge, originally proposed by Scheffler and Bonneß[2017], and extended it with semantic device descriptions. These device descriptions provide a description of the device capabilities. They are based on the oneM2M Base ontology and formalized by the Semantic Web Standards.
The novel approach is using a ontology-based device description directly on a constrained device in combination with the MQTT protocol. The bridge was extended in order to query such descriptions. Using a semantic annotation, we achieved that the device capabilities are self-descriptive, machine readable and re-usable.
The concept of a Virtual Device was introduced and implemented, based on semantic device descriptions. A Virtual Device aggregates the capabilities of all devices at the edge network and contributes therefore to the scalability. Thus, it is possible to control all devices via a single RPC call.
The model-driven NETCONF Web-Client is generated automatically from this YANG model which is generated by the bridge based on the semantic device description. The Web-Client provides a user-friendly interface, offers RPC calls and displays sensor values. We demonstrate the feasibility of this approach in different use cases: sensor and actuator scenarios, as well as event configuration and triggering.
The semantic approach results in increased memory overhead. Therefore, we evaluated CBOR and RDF HDT for optimization of ontology-based device descriptions for use on constrained devices. The evaluation shows that CBOR is not suitable for long strings and RDF HDT is a promising candidate but is still a W3C Member Submission. Finally, we used an optimized JSON-LD format for the syntax of the device descriptions.
One of the security tasks of network management is the distribution of firmware updates. The MYNO Update Protocol (MUP) was developed and evaluated on constrained devices CC2538dk and 6LoWPAN. The MYNO update process is focused on freshness and authenticity of the firmware. The evaluation shows that it is challenging but feasible to bring the firmware updates to constrained devices using MQTT. As a new requirement for the next MQTT version, we propose to add a slicing feature for the better support of constrained devices. The MQTT broker should slice data to the maximum packet size specified by the device and transfer it slice-by-slice.
For the performance and scalability evaluation of MYNO framework, we setup the High Precision Agriculture demonstrator with 10 ESP-32 NodeMCU boards at the edge of the network. The ESP-32 NodeMCU boards, connected by WLAN, were equipped with six sensors and two actuators. The performance evaluation shows that the processing of ontology-based descriptions on a Raspberry Pi 3B with the RDFLib is a challenging task regarding computational power. Nevertheless, it is feasible because it must be done only once per device during the discovery process.
The MYNO framework was tested with heterogeneous devices such as CC2538dk from Texas Instruments, Arduino Yún Rev 3, and ESP-32 NodeMCU, and IP-based networks such as 6LoWPAN and WLAN.
Summarizing, with the MYNO framework we could show that the semantic approach on constrained devices is feasible in the IoT.
Institutionelle Bildung ist für autistische Lernende mit vielgestaltigen und spezifischen Hindernissen verbunden. Dies gilt insbesondere im Zusammenhang mit Inklusion, deren Relevanz nicht zuletzt durch das Übereinkommen der Vereinten Nationen über die Rechte von Menschen mit Behinderung gegeben ist.
Diese Arbeit diskutiert zahlreiche lernrelevante Besonderheiten im Kontext von Autismus und zeigt Diskrepanzen zu den nicht immer ausreichend angemessenen institutionellen Lehrkonzepten. Eine zentrale These ist hierbei, dass die ungewöhnlich intensive Aufmerksamkeit von Autist*innen für ihre Spezialinteressen dafür genutzt werden kann, das Lernen mit fremdgestellten Inhalten zu erleichtern. Darauf aufbauend werden Lösungsansätze diskutiert, welche in einem neuartigen Konzept für ein digitales mehrgerätebasiertes Lernspiel resultieren.
Eine wesentliche Herausforderung bei der Konzeption spielbasierten Lernens besteht in der adäquaten Einbindung von Lerninhalten in einen fesselnden narrativen Kontext. Am Beispiel von Übungen zur emotionalen Deutung von Mimik, welche für das Lernen von sozioemotionalen Kompetenzen besonders im Rahmen von Therapiekonzepten bei Autismus Verwendung finden, wird eine angemessene Narration vorgestellt, welche die störungsarme Einbindung dieser sehr speziellen Lerninhalte ermöglicht.
Die Effekte der einzelnen Konzeptionselemente werden anhand eines prototypisch entwickelten Lernspiels untersucht. Darauf aufbauend zeigt eine quantitative Studie die gute Akzeptanz und Nutzerfreundlichkeit des Spiels und belegte vor allem die
Verständlichkeit der Narration und der Spielelemente. Ein weiterer Schwerpunkt liegt in der minimalinvasiven Untersuchung möglicher Störungen des Spielerlebnisses durch den Wechsel zwischen verschiedenen Endgeräten, für die ein innovatives Messverfahren entwickelt wurde.
Im Ergebnis beleuchtet diese Arbeit die Bedeutung und die Grenzen von spielbasierten Ansätzen für autistische Lernende. Ein großer Teil der vorgestellten Konzepte lässt sich auf andersartige Lernszenarien übertragen. Das dafür entwickelte technische Framework zur Realisierung narrativer Lernpfade ist ebenfalls darauf vorbereitet, für weitere Lernszenarien, gerade auch im institutionellen Kontext, Verwendung zu finden.
In the last decades, there was a notable progress in solving the well-known Boolean satisfiability (Sat) problem, which can be witnessed by powerful Sat solvers. One of the reasons why these solvers are so fast are structural properties of instances that are utilized by the solver’s interna. This thesis deals with the well-studied structural property treewidth, which measures the closeness of an instance to being a tree. In fact, there are many problems parameterized by treewidth that are solvable in polynomial time in the instance size when parameterized by treewidth.
In this work, we study advanced treewidth-based methods and tools for problems in knowledge representation and reasoning (KR). Thereby, we provide means to establish precise runtime results (upper bounds) for canonical problems relevant to KR. Then, we present a new type of problem reduction, which we call decomposition-guided (DG) that
allows us to precisely monitor the treewidth when reducing from one problem to another problem. This new reduction type will be the basis for a long-open lower bound result for quantified Boolean formulas and allows us to design a new methodology for establishing runtime lower bounds for problems parameterized by treewidth.
Finally, despite these lower bounds, we provide an efficient implementation of algorithms that adhere to treewidth. Our approach finds suitable abstractions of instances, which are subsequently refined in a recursive fashion, and it uses Sat solvers for solving subproblems. It turns out that our resulting solver is quite competitive for two canonical counting problems related to Sat.
Discriminative Models for Biometric Identification using Micro- and Macro-Movements of the Eyes
(2021)
Human visual perception is an active process. Eye movements either alternate between fixations and saccades or follow a smooth pursuit movement in case of moving targets. Besides these macroscopic gaze patterns, the eyes perform involuntary micro-movements during fixations which are commonly categorized into micro-saccades, drift and tremor. Eye movements are frequently studied in cognitive psychology, because they reflect a complex interplay of perception, attention and oculomotor control.
A common insight of psychological research is that macro-movements are highly individual. Inspired by this finding, there has been a considerable amount of prior research on oculomotoric biometric identification. However, the accuracy of known approaches is too low and the time needed for identification is too long for any practical application. This thesis explores discriminative models for the task of biometric identification.
Discriminative models optimize a quality measure of the predictions and are usually superior to generative approaches in discriminative tasks. However, using discriminative models requires to select a suitable form of data representation for sequential eye gaze data; i.e., by engineering features or constructing a sequence kernel and the performance of the classification model strongly depends on the data representation. We study two fundamentally different ways of representing eye gaze within a discriminative framework. In the first part of this thesis, we explore the integration of data and psychological background knowledge in the form of generative models to construct representations. To this end, we first develop generative statistical models of gaze behavior during reading and scene viewing that account for viewer-specific distributional properties of gaze patterns. In a second step, we develop a discriminative identification model by deriving Fisher kernel functions from these and several baseline models. We find that an SVM with Fisher kernel is able to reliably identify users based on their eye gaze during reading and scene viewing. However, since the generative models are constrained to use low-frequency macro-movements, they discard a significant amount of information contained in the raw eye tracking signal at a high cost: identification requires about one minute of input recording, which makes it inapplicable for real world biometric systems. In the second part of this thesis, we study a purely data-driven modeling approach. Here, we aim at automatically discovering the individual pattern hidden in the raw eye tracking signal. To this end, we develop a deep convolutional neural network DeepEyedentification that processes yaw and pitch gaze velocities and learns a representation end-to-end. Compared to prior work, this model increases the identification accuracy by one order of magnitude and the time to identification decreases to only seconds. The DeepEyedentificationLive model further improves upon the identification performance by processing binocular input and it also detects presentation-attacks.
We find that by learning a representation, the performance of oculomotoric identification and presentation-attack detection can be driven close to practical relevance for biometric applications. Eye tracking devices with high sampling frequency and precision are expensive and the applicability of eye movement as a biometric feature heavily depends on cost of recording devices.
In the last part of this thesis, we therefore study the requirements on data quality by evaluating the performance of the DeepEyedentificationLive network under reduced spatial and temporal resolution. We find that the method still attains a high identification accuracy at a temporal resolution of only 250 Hz and a precision of 0.03 degrees. Reducing both does not have an additive deteriorating effect.
The business problem of having inefficient processes, imprecise process analyses, and simulations as well as non-transparent artificial neuronal network models can be overcome by an easy-to-use modeling concept. With the aim of developing a flexible and efficient approach to modeling, simulating, and optimizing processes, this paper proposes a flexible Concept of Neuronal Modeling (CoNM). The modeling concept, which is described by the modeling language designed and its mathematical formulation and is connected to a technical substantiation, is based on a collection of novel sub-artifacts. As these have been implemented as a computational model, the set of CoNM tools carries out novel kinds of Neuronal Process Modeling (NPM), Neuronal Process Simulations (NPS), and Neuronal Process Optimizations (NPO). The efficacy of the designed artifacts was demonstrated rigorously by means of six experiments and a simulator of real industrial production processes.
Digitalisierung ermöglicht es uns, mit Partnern (z.B. Unternehmen, Institutionen) in einer IT-unterstützten Umgebung zu interagieren und Tätigkeiten auszuführen, die vormals manuell erledigt wurden. Ein Ziel der Digitalisierung ist dabei, Dienstleistungen unterschiedlicher fachlicher Domänen zu Prozessen zu kombinieren und vielen Nutzergruppen bedarfsgerecht zugänglich zu machen. Hierzu stellen Anbieter technische Dienste bereit, die in unterschiedliche Anwendungen integriert werden können.
Die Digitalisierung stellt die Anwendungsentwicklung vor neue Herausforderungen. Ein Aspekt ist die bedarfsgerechte Anbindung von Nutzern an Dienste. Zur Interaktion menschlicher Nutzer mit den Diensten werden Benutzungsschnittstellen benötigt, die auf deren Bedürfnisse zugeschnitten sind. Hierzu werden Varianten für spezifische Nutzergruppen (fachliche Varianten) und variierende Umgebungen (technische Varianten) benötigt. Zunehmend müssen diese mit Diensten anderer Anbieter kombiniert werden können, um domänenübergreifend Prozesse zu Anwendungen mit einem erhöhten Mehrwert für den Endnutzer zu verknüpfen (z.B. eine Flugbuchung mit einer optionalen Reiseversicherung).
Die Vielfältigkeit der Varianten lässt die Erstellung von Benutzungsschnittstellen komplex und die Ergebnisse sehr individuell erscheinen. Daher werden die Varianten in der Praxis vorwiegend manuell erstellt. Dies führt zur parallelen Entwicklung einer Vielzahl sehr ähnlicher Anwendungen, die nur geringes Potential zur Wiederverwendung besitzen. Die Folge sind hohe Aufwände bei Erstellung und Wartung. Dadurch wird häufig auf die Unterstützung kleiner Nutzerkreise mit speziellen Anforderungen verzichtet (z.B. Menschen mit physischen Einschränkungen), sodass diese weiterhin von der Digitalisierung ausgeschlossen bleiben.
Die Arbeit stellt eine konsistente Lösung für diese neuen Herausforderungen mit den Mitteln der modellgetriebenen Entwicklung vor. Sie präsentiert einen Ansatz zur Modellierung von Benutzungsschnittstellen, Varianten und Kompositionen und deren automatischer Generierung für digitale Dienste in einem verteilten Umfeld. Die Arbeit schafft eine Lösung zur Wiederverwendung und gemeinschaftlichen Nutzung von Benutzungsschnittstellen über Anbietergrenzen hinweg. Sie führt zu einer Infrastruktur, in der eine Vielzahl von Anbietern ihre Expertise in gemeinschaftliche Anwendungen einbringen können.
Die Beiträge bestehen im Einzelnen in Konzepten und Metamodellen zur Modellierung von Benutzungsschnittstellen, Varianten und Kompositionen sowie einem Verfahren zu deren vollständig automatisierten Transformation in funktionale Benutzungsschnittstellen. Zur Umsetzung der gemeinschaftlichen Nutzbarkeit werden diese ergänzt um eine universelle Repräsentation der Modelle, einer Methodik zur Anbindung unterschiedlicher Dienst-Anbieter sowie einer Architektur zur verteilten Nutzung der Artefakte und Verfahren in einer dienstorientierten Umgebung.
Der Ansatz bietet die Chance, unterschiedlichste Menschen bedarfsgerecht an der Digitalisierung teilhaben zu lassen. Damit setzt die Arbeit Impulse für zukünftige Methoden zur Anwendungserstellung in einem zunehmend vielfältigen Umfeld.
Zum Einfluss von Adaptivität auf die Wahrnehmung von Komplexität in der Mensch-Technik-Interaktion
(2021)
Wir leben in einer Gesellschaft, die von einem stetigen Wunsch nach Innovation und Fortschritt geprägt ist. Folgen dieses Wunsches sind die immer weiter fortschreitende Digitalisierung und informatische Vernetzung aller Lebensbereiche, die so zu immer komplexeren sozio-technischen Systemen führen. Ziele dieser Systeme sind u. a. die Unterstützung von Menschen, die Verbesserung ihrer Lebenssituation oder Lebensqualität oder die Erweiterung menschlicher Möglichkeiten. Doch haben neue komplexe technische Systeme nicht nur positive soziale und gesellschaftliche Effekte. Oft gibt es unerwünschte Nebeneffekte, die erst im Gebrauch sichtbar werden, und sowohl Konstrukteur*innen als auch Nutzer*innen komplexer vernetzter Technologien fühlen sich oft orientierungslos. Die Folgen können von sinkender Akzeptanz bis hin zum kompletten Verlust des Vertrauens in vernetze Softwaresysteme reichen. Da komplexe Anwendungen, und damit auch immer komplexere Mensch-Technik-Interaktionen, immer mehr an Relevanz gewinnen, ist es umso wichtiger, wieder Orientierung zu finden. Dazu müssen wir zuerst diejenigen Elemente identifizieren, die in der Interaktion mit vernetzten sozio-technischen Systemen zu Komplexität beitragen und somit Orientierungsbedarf hervorrufen.
Mit dieser Arbeit soll ein Beitrag geleistet werden, um ein strukturiertes Reflektieren über die Komplexität vernetzter sozio-technischer Systeme im gesamten Konstruktionsprozess zu ermöglichen. Dazu wird zuerst eine Definition von Komplexität und komplexen Systemen erarbeitet, die über das informatische Verständnis von Komplexität (also der Kompliziertheit von Problemen, Algorithmen oder Daten) hinausgeht. Im Vordergrund soll vielmehr die sozio-technische Interaktion mit und in komplexen vernetzten Systemen stehen. Basierend auf dieser Definition wird dann ein Analysewerkzeug entwickelt, welches es ermöglicht, die Komplexität in der Interaktion mit sozio-technischen Systemen sichtbar und beschreibbar zu machen.
Ein Bereich, in dem vernetzte sozio-technische Systeme zunehmenden Einzug finden, ist jener digitaler Bildungstechnologien. Besonders adaptiven Bildungstechnologien wurde in den letzten Jahrzehnten ein großes Potential zugeschrieben. Zwei adaptive Lehr- bzw. Trainingssysteme sollen deshalb exemplarisch mit dem in dieser Arbeit entwickelten Analysewerkzeug untersucht werden. Hierbei wird ein besonderes Augenmerkt auf den Einfluss von Adaptivität auf die Komplexität von Mensch-Technik-Interaktionssituationen gelegt. In empirischen Untersuchungen werden die Erfahrungen von Konstrukteur*innen und Nutzer*innen jener adaptiver Systeme untersucht, um so die entscheidenden Kriterien für Komplexität ermitteln zu können. Auf diese Weise können zum einen wiederkehrende Orientierungsfragen bei der Entwicklung adaptiver Bildungstechnologien aufgedeckt werden. Zum anderen werden als komplex wahrgenommene Interaktionssituationen identifiziert. An diesen Situationen kann gezeigt werden, wo aufgrund der Komplexität des Systems die etablierten Alltagsroutinen von Nutzenden nicht mehr ausreichen, um die Folgen der Interaktion mit dem System vollständig erfassen zu können. Dieses Wissen kann sowohl Konstrukteur*innen als auch Nutzer*innen helfen, in Zukunft besser mit der inhärenten Komplexität moderner Bildungstechnologien umzugehen.
Forschendes Lernen und die digitale Transformation sind zwei der wichtigsten Einflüsse auf die Entwicklung der Hochschuldidaktik im deutschprachigen Raum. Während das forschende Lernen als normative Theorie das sollen beschreibt, geben die digitalen Werkzeuge, alte wie neue, das können in vielen Bereichen vor.
In der vorliegenden Arbeit wird ein Prozessmodell aufgestellt, was den Versuch unternimmt, das forschende Lernen hinsichtlich interaktiver, gruppenbasierter Prozesse zu systematisieren. Basierend auf dem entwickelten Modell wurde ein Softwareprototyp implementiert, der den gesamten Forschungsprozess begleiten kann. Dabei werden Gruppenformation, Feedback- und Reflexionsprozesse und das Peer Assessment mit Bildungstechnologien unterstützt. Die Entwicklungen wurden in einem qualitativen Experiment eingesetzt, um Systemwissen über die Möglichkeiten und Grenzen der digitalen Unterstützung von forschendem Lernen zu gewinnen.
TrainTrap
(2020)
Die Fehlerkorrektur in der Codierungstheorie beschäftigt sich mit der Erkennung und Behebung von Fehlern bei der Übertragung und auch Sicherung von Nachrichten.
Hierbei wird die Nachricht durch zusätzliche Informationen in ein Codewort kodiert.
Diese Kodierungsverfahren besitzen verschiedene Ansprüche, wie zum Beispiel die maximale Anzahl der zu korrigierenden Fehler und die Geschwindigkeit der Korrektur.
Ein gängiges Codierungsverfahren ist der BCH-Code, welches industriell für bis zu vier Fehler korrigiere Codes Verwendung findet. Ein Nachteil dieser Codes ist die technische Durchlaufzeit für die Berechnung der Fehlerstellen mit zunehmender Codelänge.
Die Dissertation stellt ein neues Codierungsverfahren vor, bei dem durch spezielle Anordnung kleinere Codelängen eines BCH-Codes ein langer Code erzeugt wird. Diese Anordnung geschieht über einen weiteren speziellen Code, einem LDPC-Code, welcher für eine schneller Fehlererkennung konzipiert ist.
Hierfür wird ein neues Konstruktionsverfahren vorgestellt, welches einen Code für einen beliebige Länge mit vorgebbaren beliebigen Anzahl der zu korrigierenden Fehler vorgibt. Das vorgestellte Konstruktionsverfahren erzeugt zusätzlich zum schnellen Verfahren der Fehlererkennung auch eine leicht und schnelle Ableitung eines Verfahrens zu Kodierung der Nachricht zum Codewort. Dies ist in der Literatur für die LDPC-Codes bis zum jetzigen Zeitpunkt einmalig.
Durch die Konstruktion eines LDPC-Codes wird ein Verfahren vorgestellt wie dies mit einem BCH-Code kombiniert wird, wodurch eine Anordnung des BCH-Codes in Blöcken erzeugt wird. Neben der allgemeinen Beschreibung dieses Codes, wird ein konkreter Code für eine 2-Bitfehlerkorrektur beschrieben. Diese besteht aus zwei Teilen, welche in verschiedene Varianten beschrieben und verglichen werden. Für bestimmte Längen des BCH-Codes wird ein Problem bei der Korrektur aufgezeigt, welche einer algebraischen Regel folgt.
Der BCH-Code wird sehr allgemein beschrieben, doch existiert durch bestimmte Voraussetzungen ein BCH-Code im engerem Sinne, welcher den Standard vorgibt. Dieser BCH-Code im engerem Sinne wird in dieser Dissertation modifiziert, so dass das algebraische Problem bei der 2-Bitfehler Korrektur bei der Kombination mit dem LDPC-Code nicht mehr existiert. Es wird gezeigt, dass nach der Modifikation der neue Code weiterhin ein BCH-Code im allgemeinen Sinne ist, welcher 2-Bitfehler korrigieren und 3-Bitfehler erkennen kann. Bei der technischen Umsetzung der Fehlerkorrektur wird des Weiteren gezeigt, dass die Durchlaufzeiten des modifizierten Codes im Vergleich zum BCH-Code schneller ist und weiteres Potential für Verbesserungen besitzt.
Im letzten Kapitel wird gezeigt, dass sich dieser modifizierte Code mit beliebiger Länge eignet für die Kombination mit dem LDPC-Code, wodurch dieses Verfahren nicht nur umfänglicher in der Länge zu nutzen ist, sondern auch durch die schnellere Dekodierung auch weitere Vorteile gegenüber einem BCH-Code im engerem Sinne besitzt.
A central insight from psychological studies on human eye movements is that eye movement patterns are highly individually characteristic. They can, therefore, be used as a biometric feature, that is, subjects can be identified based on their eye movements. This thesis introduces new machine learning methods to identify subjects based on their eye movements while viewing arbitrary content. The thesis focuses on probabilistic modeling of the problem, which has yielded the best results in the most recent literature. The thesis studies the problem in three phases by proposing a purely probabilistic, probabilistic deep learning, and probabilistic deep metric learning approach. In the first phase, the thesis studies models that rely on psychological concepts about eye movements. Recent literature illustrates that individual-specific distributions of gaze patterns can be used to accurately identify individuals. In these studies, models were based on a simple parametric family of distributions. Such simple parametric models can be robustly estimated from sparse data, but have limited flexibility to capture the differences between individuals. Therefore, this thesis proposes a semiparametric model of gaze patterns that is flexible yet robust for individual identification. These patterns can be understood as domain knowledge derived from psychological literature. Fixations and saccades are examples of simple gaze patterns. The proposed semiparametric densities are drawn under a Gaussian process prior centered at a simple parametric distribution. Thus, the model will stay close to the parametric class of densities if little data is available, but it can also deviate from this class if enough data is available, increasing the flexibility of the model. The proposed method is evaluated on a large-scale dataset, showing significant improvements over the state-of-the-art. Later, the thesis replaces the model based on gaze patterns derived from psychological concepts with a deep neural network that can learn more informative and complex patterns from raw eye movement data. As previous work has shown that the distribution of these patterns across a sequence is informative, a novel statistical aggregation layer called the quantile layer is introduced. It explicitly fits the distribution of deep patterns learned directly from the raw eye movement data. The proposed deep learning approach is end-to-end learnable, such that the deep model learns to extract informative, short local patterns while the quantile layer learns to approximate the distributions of these patterns. Quantile layers are a generic approach that can converge to standard pooling layers or have a more detailed description of the features being pooled, depending on the problem. The proposed model is evaluated in a large-scale study using the eye movements of subjects viewing arbitrary visual input. The model improves upon the standard pooling layers and other statistical aggregation layers proposed in the literature. It also improves upon the state-of-the-art eye movement biometrics by a wide margin. Finally, for the model to identify any subject — not just the set of subjects it is trained on — a metric learning approach is developed. Metric learning learns a distance function over instances. The metric learning model maps the instances into a metric space, where sequences of the same individual are close, and sequences of different individuals are further apart. This thesis introduces a deep metric learning approach with distributional embeddings. The approach represents sequences as a set of continuous distributions in a metric space; to achieve this, a new loss function based on Wasserstein distances is introduced. The proposed method is evaluated on multiple domains besides eye movement biometrics. This approach outperforms the state of the art in deep metric learning in several domains while also outperforming the state of the art in eye movement biometrics.
Emotions are a central element of human experience. They occur with high frequency in everyday life and play an important role in decision making. However, currently there is no consensus among researchers on what constitutes an emotion and on how emotions should be investigated. This dissertation identifies three problems of current emotion research: the problem of ground truth, the problem of incomplete constructs and the problem of optimal representation. I argue for a focus on the detailed measurement of emotion manifestations with computer-aided methods to solve these problems. This approach is demonstrated in three research projects, which describe the development of methods specific to these problems as well as their application to concrete research questions.
The problem of ground truth describes the practice to presuppose a certain structure of emotions as the a priori ground truth. This determines the range of emotion descriptions and sets a standard for the correct assignment of these descriptions. The first project illustrates how this problem can be circumvented with a multidimensional emotion perception paradigm which stands in contrast to the emotion recognition paradigm typically employed in emotion research. This paradigm allows to calculate an objective difficulty measure and to collect subjective difficulty ratings for the perception of emotional stimuli. Moreover, it enables the use of an arbitrary number of emotion stimuli categories as compared to the commonly used six basic emotion categories. Accordingly, we collected data from 441 participants using dynamic facial expression stimuli from 40 emotion categories. Our findings suggest an increase in emotion perception difficulty with increasing actor age and provide evidence to suggest that young adults, the elderly and men underestimate their emotion perception difficulty. While these effects were predicted from the literature, we also found unexpected and novel results. In particular, the increased difficulty on the objective difficulty measure for female actors and observers stood in contrast to reported findings. Exploratory analyses revealed low relevance of person-specific variables for the prediction of emotion perception difficulty, but highlighted the importance of a general pleasure dimension for the ease of emotion perception.
The second project targets the problem of incomplete constructs which relates to vaguely defined psychological constructs on emotion with insufficient ties to tangible manifestations. The project exemplifies how a modern data collection method such as face tracking data can be used to sharpen these constructs on the example of arousal, a long-standing but fuzzy construct in emotion research. It describes how measures of distance, speed and magnitude of acceleration can be computed from face tracking data and investigates their intercorrelations. We find moderate to strong correlations among all measures of static information on one hand and all measures of dynamic information on the other. The project then investigates how self-rated arousal is tied to these measures in 401 neurotypical individuals and 19 individuals with autism. Distance to the neutral face was predictive of arousal ratings in both groups. Lower mean arousal ratings were found for the autistic group, but no difference in correlation of the measures and arousal ratings could be found between groups. Results were replicated in a high autistic traits group consisting of 41 participants. The findings suggest a qualitatively similar perception of arousal for individuals with and without autism. No correlations between valence ratings and any of the measures could be found which emphasizes the specificity of our tested measures for the construct of arousal.
The problem of optimal representation refers to the search for the best representation of emotions and the assumption that there is a one-fits-all solution. In the third project we introduce partial least squares analysis as a general method to find an optimal representation to relate two high-dimensional data sets to each other. The project demonstrates its applicability to emotion research on the question of emotion perception differences between men and women. The method was used with emotion rating data from 441 participants and face tracking data computed on 306 videos. We found quantitative as well as qualitative differences in the perception of emotional facial expressions between these groups. We showed that women’s emotional perception systematically captured more of the variance in facial expressions. Additionally, we could show that significant differences exist in the way that women and men perceive some facial expressions which could be visualized as concrete facial expression sequences. These expressions suggest differing perceptions of masked and ambiguous facial expressions between the sexes. In order to facilitate use of the developed method by the research community, a package for the statistical environment R was written. Furthermore, to call attention to the method and its usefulness for emotion research, a website was designed that allows users to explore a model of emotion ratings and facial expression data in an interactive fashion.
The usage of mobile devices is rapidly growing with Android being the most prevalent mobile operating system. Thanks to the vast variety of mobile applications, users are preferring smartphones over desktops for day to day tasks like Internet surfing. Consequently, smartphones store a plenitude of sensitive data. This data together with the high values of smartphones make them an attractive target for device/data theft (thieves/malicious applications).
Unfortunately, state-of-the-art anti-theft solutions do not work if they do not have an active network connection, e.g., if the SIM card was removed from the device. In the majority of these cases, device owners permanently lose their smartphone together with their personal data, which is even worse.
Apart from that malevolent applications perform malicious activities to steal sensitive information from smartphones. Recent research considered static program analysis to detect dangerous data leaks. These analyses work well for data leaks due to inter-component communication, but suffer from shortcomings for inter-app communication with respect to precision, soundness, and scalability.
This thesis focuses on enhancing users' privacy on Android against physical device loss/theft and (un)intentional data leaks. It presents three novel frameworks: (1) ThiefTrap, an anti-theft framework for Android, (2) IIFA, a modular inter-app intent information flow analysis of Android applications, and (3) PIAnalyzer, a precise approach for PendingIntent vulnerability analysis.
ThiefTrap is based on a novel concept of an anti-theft honeypot account that protects the owner's data while preventing a thief from resetting the device.
We implemented the proposed scheme and evaluated it through an empirical user study with 35 participants. In this study, the owner's data could be protected, recovered, and anti-theft functionality could be performed unnoticed from the thief in all cases.
IIFA proposes a novel approach for Android's inter-component/inter-app communication (ICC/IAC) analysis. Our main contribution is the first fully automatic, sound, and precise ICC/IAC information flow analysis that is scalable for realistic apps due to modularity, avoiding combinatorial explosion: Our approach determines communicating apps using short summaries rather than inlining intent calls between components and apps, which requires simultaneously analyzing all apps installed on a device.
We evaluate IIFA in terms of precision, recall, and demonstrate its scalability to a large corpus of real-world apps. IIFA reports 62 problematic ICC-/IAC-related information flows via two or more apps/components.
PIAnalyzer proposes a novel approach to analyze PendingIntent related vulnerabilities. PendingIntents are a powerful and universal feature of Android for inter-component communication. We empirically evaluate PIAnalyzer on a set of 1000 randomly selected applications and find 1358 insecure usages of PendingIntents, including 70 severe vulnerabilities.
Die Projektierung und Abwicklung sowie die statische und dynamische Analyse von Geschäftsprozessen im Bereich des Verwaltens und Regierens auf kommunaler, Länder- wie auch Bundesebene mit Hilfe von Informations- und Kommunikationstechniken beschäftigen Politiker und Strategen für Informationstechnologie ebenso wie die Öffentlichkeit seit Langem.
Der hieraus entstandene Begriff E-Government wurde in der Folge aus den unterschiedlichsten technischen, politischen und semantischen Blickrichtungen beleuchtet.
Die vorliegende Arbeit konzentriert sich dabei auf zwei Schwerpunktthemen:
• Das erste Schwerpunktthema behandelt den Entwurf eines hierarchischen Architekturmodells, für welches sieben hierarchische Schichten identifiziert werden können. Diese erscheinen notwendig, aber auch hinreichend, um den allgemeinen Fall zu beschreiben.
Den Hintergrund hierfür liefert die langjährige Prozess- und Verwaltungserfahrung als Leiter der EDV-Abteilung der Stadtverwaltung Landshut, eine kreisfreie Stadt mit rund 69.000 Einwohnern im Nordosten von München. Sie steht als Repräsentant für viele Verwaltungsvorgänge in der Bundesrepublik Deutschland und ist dennoch als Analyseobjekt in der Gesamtkomplexität und Prozessquantität überschaubar.
Somit können aus der Analyse sämtlicher Kernabläufe statische und dynamische Strukturen extrahiert und abstrakt modelliert werden.
Die Schwerpunkte liegen in der Darstellung der vorhandenen Bedienabläufe in einer Kommune. Die Transformation der Bedienanforderung in einem hierarchischen System, die Darstellung der Kontroll- und der Operationszustände in allen Schichten wie auch die Strategie der Fehlererkennung und Fehlerbehebung schaffen eine transparente Basis für umfassende Restrukturierungen und Optimierungen.
Für die Modellierung wurde FMC-eCS eingesetzt, eine am Hasso-Plattner-Institut für Softwaresystemtechnik GmbH (HPI) im Fachgebiet Kommunikationssysteme entwickelte Methodik zur Modellierung zustandsdiskreter Systeme unter Berücksichtigung möglicher Inkonsistenzen (Betreuer: Prof. Dr.-Ing. Werner Zorn [ZW07a, ZW07b]).
• Das zweite Schwerpunktthema widmet sich der quantitativen Modellierung und Optimierung von E-Government-Bediensystemen, welche am Beispiel des Bürgerbüros der Stadt Landshut im Zeitraum 2008 bis 2015 durchgeführt wurden. Dies erfolgt auf Basis einer kontinuierlichen Betriebsdatenerfassung mit aufwendiger Vorverarbeitung zur Extrahierung mathematisch beschreibbarer Wahrscheinlichkeitsverteilungen.
Der hieraus entwickelte Dienstplan wurde hinsichtlich der erzielbaren Optimierungen im dauerhaften Echteinsatz verifiziert.
[ZW07a] Zorn, Werner: «FMC-QE A New Approach in Quantitative Modeling», Vortrag anlässlich: MSV'07- The 2007 International Conference on Modeling, Simulation and Visualization Methods WorldComp2007, Las Vegas, 28.6.2007.
[ZW07b] Zorn, Werner: «FMC-QE, A New Approach in Quantitative Modeling», Veröffentlichung, Hasso-Plattner-Institut für Softwaresystemtechnik an der Universität Potsdam, 28.6.2007.
In this thesis we introduce the concept of the degree of formality. It is directed against a dualistic point of view, which only distinguishes between formal and informal proofs. This dualistic attitude does not respect the differences between the argumentations classified as informal and it is unproductive because the individual potential of the respective argumentation styles cannot be appreciated and remains untapped.
This thesis has two parts. In the first of them we analyse the concept of the degree of formality (including a discussion about the respective benefits for each degree) while in the second we demonstrate its usefulness in three case studies. In the first case study we will repair Haskell B. Curry's view of mathematics, which incidentally is of great importance in the first part of this thesis, in light of the different degrees of formality. In the second case study we delineate how awareness of the different degrees of formality can be used to help students to learn how to prove. Third, we will show how the advantages of proofs of different degrees of formality can be combined by the development of so called tactics having a medium degree of formality. Together the three case studies show that the degrees of formality provide a convincing solution to the problem of untapped potential.
Physical computing covers the design and realization of interactive objects and installations and allows learners to develop concrete, tangible products of the real world, which arise from their imagination. This can be used in computer science education to provide learners with interesting and motivating access to the different topic areas of the subject in constructionist and creative learning environments. However, if at all, physical computing has so far mostly been taught in afternoon clubs or other extracurricular settings. Thus, for the majority of students so far there are no opportunities to design and create their own interactive objects in regular school lessons.
Despite its increasing popularity also for schools, the topic has not yet been clearly and sufficiently characterized in the context of computer science education. The aim of this doctoral thesis therefore is to clarify physical computing from the perspective of computer science education and to adequately prepare the topic both content-wise and methodologically for secondary school teaching. For this purpose, teaching examples, activities, materials and guidelines for classroom use are developed, implemented and evaluated in schools.
In the theoretical part of the thesis, first the topic is examined from a technical point of view. A structured literature analysis shows that basic concepts used in physical computing can be derived from embedded systems, which are the core of a large field of different application areas and disciplines. Typical methods of physical computing in professional settings are analyzed and, from an educational perspective, elements suitable for computer science teaching in secondary schools are extracted, e. g. tinkering and prototyping. The investigation and classification of suitable tools for school teaching show that microcontrollers and mini computers, often with extensions that greatly facilitate the handling of additional components, are particularly attractive tools for secondary education. Considering the perspectives of science, teachers, students and society, in addition to general design principles, exemplary teaching approaches for school education and suitable learning materials are developed and the design, production and evaluation of a physical computing construction kit suitable for teaching is described.
In the practical part of this thesis, with “My Interactive Garden”, an exemplary approach to integrate physical computing in computer science teaching is tested and evaluated in different courses and refined based on the findings in a design-based research approach. In a series of workshops on physical computing, which is based on a concept for constructionist professional development that is developed specifically for this purpose, teachers are empowered and encouraged to develop and conduct physical computing lessons suitable for their particular classroom settings. Based on their in-class experiences, a process model of physical computing teaching is derived. Interviews with those teachers illustrate that benefits of physical computing, including the tangibility of crafted objects and creativity in the classroom, outweigh possible drawbacks like longer preparation times, technical difficulties or difficult assessment. Hurdles in the classroom are identified and possible solutions discussed.
Empirical investigations in the different settings reveal that “My Interactive Garden” and physical computing in general have a positive impact, among others, on learner motivation, fun and interest in class and perceived competencies.
Finally, the results from all evaluations are combined to evaluate the design principles for physical computing teaching and to provide a perspective on the development of decision-making aids for physical computing activities in school education.
Answer Set Programming (ASP) is a declarative problem solving approach, combining a rich yet simple modeling language with high-performance solving capabilities. Although this has already resulted in various applications, certain aspects of such applications are more naturally modeled using variables over finite domains, for accounting for resources, fine timings, coordinates, or functions. Our goal is thus to extend ASP with constraints over integers while preserving its declarative nature. This allows for fast prototyping and elaboration tolerant problem descriptions of resource related applications. The resulting paradigm is called Constraint Answer Set Programming (CASP).
We present three different approaches for solving CASP problems. The first one, a lazy, modular approach combines an ASP solver with an external system for handling constraints. This approach has the advantage that two state of the art technologies work hand in hand to solve the problem, each concentrating on its part of the problem. The drawback is that inter-constraint dependencies cannot be communicated back to the ASP solver, impeding its learning algorithm. The second approach translates all constraints to ASP. Using the appropriate encoding techniques, this results in a very fast, monolithic system. Unfortunately, due to the large, explicit representation of constraints and variables, translation techniques are restricted to small and mid-sized domains. The third approach merges the lazy and the translational approach, combining the strength of both while removing their weaknesses. To this end, we enhance the dedicated learning techniques of an ASP solver with the inferences of the translating approach in a lazy way. That is, the important knowledge is only made explicit when needed.
By using state of the art techniques from neighboring fields, we provide ways to tackle real world, industrial size problems. By extending CASP to reactive solving, we open up new application areas such as online planning with continuous domains and durations.
Das Thema der vorliegenden Arbeit ist die semantische Suche im Kontext heutiger Informationsmanagementsysteme. Zu diesen Systemen zählen Intranets, Web 3.0-Anwendungen sowie viele Webportale, die Informationen in heterogenen Formaten und Strukturen beinhalten. Auf diesen befinden sich einerseits Daten in strukturierter Form und andererseits Dokumente, die inhaltlich mit diesen Daten in Beziehung stehen. Diese Dokumente sind jedoch in der Regel nur teilweise strukturiert oder vollständig unstrukturiert. So beschreiben beispielsweise Reiseportale durch strukturierte Daten den Zeitraum, das Reiseziel, den Preis einer Reise und geben in unstrukturierter Form weitere Informationen, wie Beschreibungen zum Hotel, Zielort, Ausflugsziele an.
Der Fokus heutiger semantischer Suchmaschinen liegt auf dem Finden von Wissen entweder in strukturierter Form, auch Faktensuche genannt, oder in semi- bzw. unstrukturierter Form, was üblicherweise als semantische Dokumentensuche bezeichnet wird. Einige wenige Suchmaschinen versuchen die Lücke zwischen diesen beiden Ansätzen zu schließen. Diese durchsuchen zwar gleichzeitig strukturierte sowie unstrukturierte Daten, werten diese jedoch entweder weitgehend voneinander unabhängig aus oder schränken die Suchmöglichkeiten stark ein, indem sie beispielsweise nur bestimmte Fragemuster unterstützen. Hierdurch werden die im System verfügbaren Informationen nicht ausgeschöpft und gleichzeitig unterbunden, dass Zusammenhänge zwischen einzelnen Inhalten der jeweiligen Informationssysteme und sich ergänzende Informationen den Benutzer erreichen.
Um diese Lücke zu schließen, wurde in der vorliegenden Arbeit ein neuer hybrider semantischer Suchansatz entwickelt und untersucht, der strukturierte und semi- bzw. unstrukturierte Inhalte während des gesamten Suchprozesses kombiniert. Durch diesen Ansatz werden nicht nur sowohl Fakten als auch Dokumente gefunden, es werden auch Zusammenhänge, die zwischen den unterschiedlich strukturierten Daten bestehen, in jeder Phase der Suche genutzt und fließen in die Suchergebnisse mit ein. Liegt die Antwort zu einer Suchanfrage nicht vollständig strukturiert, in Form von Fakten, oder unstrukturiert, in Form von Dokumenten vor, so liefert dieser Ansatz eine Kombination der beiden. Die Berücksichtigung von unterschiedlich Inhalten während des gesamten Suchprozesses stellt jedoch besondere Herausforderungen an die Suchmaschine. Diese muss in der Lage sein, Fakten und Dokumente in Abhängigkeit voneinander zu durchsuchen, sie zu kombinieren sowie die unterschiedlich strukturierten Ergebnisse in eine geeignete Rangordnung zu bringen. Weiterhin darf die Komplexität der Daten nicht an die Endnutzer weitergereicht werden. Die Darstellung der Inhalte muss vielmehr sowohl bei der Anfragestellung als auch bei der Darbietung der Ergebnisse verständlich und leicht interpretierbar sein.
Die zentrale Fragestellung der Arbeit ist, ob ein hybrider Ansatz auf einer vorgegebenen Datenbasis die Suchanfragen besser beantworten kann als die semantische Dokumentensuche und die Faktensuche für sich genommen, bzw. als eine Suche die diese Ansätze im Rahmen des Suchprozesses nicht kombiniert. Die durchgeführten Evaluierungen aus System- und aus Benutzersicht zeigen, dass die im Rahmen der Arbeit entwickelte hybride semantische Suchlösung durch die Kombination von strukturierten und unstrukturierten Inhalten im Suchprozess bessere Antworten liefert als die oben genannten Verfahren und somit Vorteile gegenüber bisherigen Ansätzen bietet. Eine Befragung von Benutzern macht deutlich, dass die hybride semantische Suche als verständlich empfunden und für heterogen strukturierte Datenmengen bevorzugt wird.
Contemporary multi-core processors are parallel systems that also provide shared memory for programs running on them. Both the increasing number of cores in so-called many-core systems and the still growing computational power of the cores demand for memory systems that are able to deliver high bandwidths. Caches are essential components to satisfy this requirement. Nevertheless, hardware-based cache coherence in many-core chips faces practical limits to provide both coherence and high memory bandwidths. In addition, a shift away from global coherence can be observed. As a result, alternative architectures and suitable programming models need to be investigated.
This thesis focuses on fast communication for non-cache-coherent many-core architectures. Experiments are conducted on the Single-Chip Cloud Computer (SCC), a non-cache-coherent many-core processor with 48 mesh-connected cores. Although originally designed for message passing, the results of this thesis show that shared memory can be efficiently used for one-sided communication on this kind of architecture. One-sided communication enables data exchanges between processes where the receiver is not required to know the details of the performed communication. In the notion of the Message Passing Interface (MPI) standard, this type of communication allows to access memory of remote processes. In order to support this communication scheme on non-cache-coherent architectures, both an efficient process synchronization and a communication scheme with software-managed cache coherence are designed and investigated.
The process synchronization realizes the concept of the general active target synchronization scheme from the MPI standard. An existing classification of implementation approaches is extended and used to identify an appropriate class for the non-cache-coherent shared memory platform. Based on this classification, existing implementations are surveyed in order to find beneficial concepts, which are then used to design a lightweight synchronization protocol for the SCC that uses shared memory and uncached memory accesses. The proposed scheme is not prone to process skew and also enables direct communication as soon as both communication partners are ready. Experimental results show very good scaling properties and up to five times lower synchronization latency compared to a tuned message-based MPI implementation for the SCC.
For the communication, SCOSCo, a shared memory approach with software-managed cache coherence, is presented. According requirements for the coherence that fulfill MPI's separate memory model are formulated, and a lightweight implementation exploiting SCC hard- and software features is developed. Despite a discovered malfunction in the SCC's memory subsystem, the experimental evaluation of the design reveals up to five times better bandwidths and nearly four times lower latencies in micro-benchmarks compared to the SCC-tuned but message-based MPI library. For application benchmarks, like a parallel 3D fast Fourier transform, the runtime share of communication can be reduced by a factor of up to five. In addition, this thesis postulates beneficial hardware concepts that would support software-managed coherence for one-sided communication on future non-cache-coherent architectures where coherence might be only available in local subdomains but not on a global processor level.
Although it has become common practice to build applications based on the reuse of existing components or services, technical complexity and semantic challenges constitute barriers to ensuring a successful and wide reuse of components and services. In the geospatial application domain, the barriers are self-evident due to heterogeneous geographic data, a lack of interoperability and complex analysis processes.
Constructing workflows manually and discovering proper services and data that match user intents and preferences is difficult and time-consuming especially for users who are not trained in software development. Furthermore, considering the multi-objective nature of environmental modeling for the assessment of climate change impacts and the various types of geospatial data (e.g., formats, scales, and georeferencing systems) increases the complexity challenges.
Automatic service composition approaches that provide semantics-based assistance in the process of workflow design have proven to be a solution to overcome these challenges and have become a frequent demand especially by end users who are not IT experts. In this light, the major contributions of this thesis are:
(i) Simplification of service reuse and workflow design of applications for climate impact analysis by following the eXtreme Model-Driven Development (XMDD) paradigm.
(ii) Design of a semantic domain model for climate impact analysis applications that comprises specifically designed services, ontologies that provide domain-specific vocabulary for referring to types and services, and the input/output annotation of the services using the terms defined in the ontologies.
(iii) Application of a constraint-driven method for the automatic composition of workflows for analyzing the impacts of sea-level rise. The application scenario demonstrates the impact of domain modeling decisions on the results and the performance of the synthesis algorithm.
Simulationskonzept zur Nutzenvalidierung cyber-physischer Systeme in komplexen Fabrikumgebungen
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