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TrainTrap
(2020)
Nowadays, model-driven engineering (MDE) promises to ease software development by decreasing the inherent complexity of classical software development. In order to deliver on this promise, MDE increases the level of abstraction and automation, through a consideration of domain-specific models (DSMs) and model operations (e.g. model transformations or code generations). DSMs conform to domain-specific modeling languages (DSMLs), which increase the level of abstraction, and model operations are first-class entities of software development because they increase the level of automation. Nevertheless, MDE has to deal with at least two new dimensions of complexity, which are basically caused by the increased linguistic and technological heterogeneity. The first dimension of complexity is setting up an MDE environment, an activity comprised of the implementation or selection of DSMLs and model operations. Setting up an MDE environment is both time-consuming and error-prone because of the implementation or adaptation of model operations. The second dimension of complexity is concerned with applying MDE for actual software development. Applying MDE is challenging because a collection of DSMs, which conform to potentially heterogeneous DSMLs, are required to completely specify a complex software system. A single DSML can only be used to describe a specific aspect of a software system at a certain level of abstraction and from a certain perspective. Additionally, DSMs are usually not independent but instead have inherent interdependencies, reflecting (partial) similar aspects of a software system at different levels of abstraction or from different perspectives. A subset of these dependencies are applications of various model operations, which are necessary to keep the degree of automation high. This becomes even worse when addressing the first dimension of complexity. Due to continuous changes, all kinds of dependencies, including the applications of model operations, must also be managed continuously. This comprises maintaining the existence of these dependencies and the appropriate (re-)application of model operations. The contribution of this thesis is an approach that combines traceability and model management to address the aforementioned challenges of configuring and applying MDE for software development. The approach is considered as a traceability approach because it supports capturing and automatically maintaining dependencies between DSMs. The approach is considered as a model management approach because it supports managing the automated (re-)application of heterogeneous model operations. In addition, the approach is considered as a comprehensive model management. Since the decomposition of model operations is encouraged to alleviate the first dimension of complexity, the subsequent composition of model operations is required to counteract their fragmentation. A significant portion of this thesis concerns itself with providing a method for the specification of decoupled yet still highly cohesive complex compositions of heterogeneous model operations. The approach supports two different kinds of compositions - data-flow compositions and context compositions. Data-flow composition is used to define a network of heterogeneous model operations coupled by sharing input and output DSMs alone. Context composition is related to a concept used in declarative model transformation approaches to compose individual model transformation rules (units) at any level of detail. In this thesis, context composition provides the ability to use a collection of dependencies as context for the composition of other dependencies, including model operations. In addition, the actual implementation of model operations, which are going to be composed, do not need to implement any composition concerns. The approach is realized by means of a formalism called an executable and dynamic hierarchical megamodel, based on the original idea of megamodels. This formalism supports specifying compositions of dependencies (traceability and model operations). On top of this formalism, traceability is realized by means of a localization concept, and model management by means of an execution concept.
Im Mittelpunkt dieser Arbeit stehen virtuelle 3D-Stadtmodelle, die Objekte, Phänomene und Prozesse in urbanen Räumen in digitaler Form repräsentieren. Sie haben sich zu einem Kernthema von Geoinformationssystemen entwickelt und bilden einen zentralen Bestandteil geovirtueller 3D-Welten. Virtuelle 3D-Stadtmodelle finden nicht nur Verwendung als Mittel für Experten in Bereichen wie Stadtplanung, Funknetzplanung, oder Lärmanalyse, sondern auch für allgemeine Nutzer, die realitätsnah dargestellte virtuelle Städte in Bereichen wie Bürgerbeteiligung, Tourismus oder Unterhaltung nutzen und z. B. in Anwendungen wie GoogleEarth eine räumliche Umgebung intuitiv erkunden und durch eigene 3D-Modelle oder zusätzliche Informationen erweitern. Die Erzeugung und Darstellung virtueller 3D-Stadtmodelle besteht aus einer Vielzahl von Prozessschritten, von denen in der vorliegenden Arbeit zwei näher betrachtet werden: Texturierung und Visualisierung. Im Bereich der Texturierung werden Konzepte und Verfahren zur automatischen Ableitung von Fototexturen aus georeferenzierten Schrägluftbildern sowie zur Speicherung oberflächengebundener Daten in virtuellen 3D-Stadtmodellen entwickelt. Im Bereich der Visualisierung werden Konzepte und Verfahren für die multiperspektivische Darstellung sowie für die hochqualitative Darstellung nichtlinearer Projektionen virtueller 3D-Stadtmodelle in interaktiven Systemen vorgestellt. Die automatische Ableitung von Fototexturen aus georeferenzierten Schrägluftbildern ermöglicht die Veredelung vorliegender virtueller 3D-Stadtmodelle. Schrägluftbilder bieten sich zur Texturierung an, da sie einen Großteil der Oberflächen einer Stadt, insbesondere Gebäudefassaden, mit hoher Redundanz erfassen. Das Verfahren extrahiert aus dem verfügbaren Bildmaterial alle Ansichten einer Oberfläche und fügt diese pixelpräzise zu einer Textur zusammen. Durch Anwendung auf alle Oberflächen wird das virtuelle 3D-Stadtmodell flächendeckend texturiert. Der beschriebene Ansatz wurde am Beispiel des offiziellen Berliner 3D-Stadtmodells sowie der in GoogleEarth integrierten Innenstadt von München erprobt. Die Speicherung oberflächengebundener Daten, zu denen auch Texturen zählen, wurde im Kontext von CityGML, einem international standardisierten Datenmodell und Austauschformat für virtuelle 3D-Stadtmodelle, untersucht. Es wird ein Datenmodell auf Basis computergrafischer Konzepte entworfen und in den CityGML-Standard integriert. Dieses Datenmodell richtet sich dabei an praktischen Anwendungsfällen aus und lässt sich domänenübergreifend verwenden. Die interaktive multiperspektivische Darstellung virtueller 3D-Stadtmodelle ergänzt die gewohnte perspektivische Darstellung nahtlos um eine zweite Perspektive mit dem Ziel, den Informationsgehalt der Darstellung zu erhöhen. Diese Art der Darstellung ist durch die Panoramakarten von H. C. Berann inspiriert; Hauptproblem ist die Übertragung des multiperspektivischen Prinzips auf ein interaktives System. Die Arbeit stellt eine technische Umsetzung dieser Darstellung für 3D-Grafikhardware vor und demonstriert die Erweiterung von Vogel- und Fußgängerperspektive. Die hochqualitative Darstellung nichtlinearer Projektionen beschreibt deren Umsetzung auf 3D-Grafikhardware, wobei neben der Bildwiederholrate die Bildqualität das wesentliche Entwicklungskriterium ist. Insbesondere erlauben die beiden vorgestellten Verfahren, dynamische Geometrieverfeinerung und stückweise perspektivische Projektionen, die uneingeschränkte Nutzung aller hardwareseitig verfügbaren, qualitätssteigernden Funktionen wie z.~B. Bildraumgradienten oder anisotroper Texturfilterung. Beide Verfahren sind generisch und unterstützen verschiedene Projektionstypen. Sie ermöglichen die anpassungsfreie Verwendung gängiger computergrafischer Effekte wie Stilisierungsverfahren oder prozeduraler Texturen für nichtlineare Projektionen bei optimaler Bildqualität. Die vorliegende Arbeit beschreibt wesentliche Technologien für die Verarbeitung virtueller 3D-Stadtmodelle: Zum einen lassen sich mit den Ergebnissen der Arbeit Texturen für virtuelle 3D-Stadtmodelle automatisiert herstellen und als eigenständige Attribute in das virtuelle 3D-Stadtmodell einfügen. Somit trägt diese Arbeit dazu bei, die Herstellung und Fortführung texturierter virtueller 3D-Stadtmodelle zu verbessern. Zum anderen zeigt die Arbeit Varianten und technische Lösungen für neuartige Projektionstypen für virtueller 3D-Stadtmodelle in interaktiven Visualisierungen. Solche nichtlinearen Projektionen stellen Schlüsselbausteine dar, um neuartige Benutzungsschnittstellen für und Interaktionsformen mit virtuellen 3D-Stadtmodellen zu ermöglichen, insbesondere für mobile Geräte und immersive Umgebungen.
With increasing number of applications in Internet and mobile environments, distributed software systems are demanded to be more powerful and flexible, especially in terms of dynamism and security. This dissertation describes my work concerning three aspects: dynamic reconfiguration of component software, security control on middleware applications, and web services dynamic composition. Firstly, I proposed a technology named Routing Based Workflow (RBW) to model the execution and management of collaborative components and realize temporary binding for component instances. The temporary binding means component instances are temporarily loaded into a created execution environment to execute their functions, and then are released to their repository after executions. The temporary binding allows to create an idle execution environment for all collaborative components, on which the change operations can be immediately carried out. The changes on execution environment will result in a new collaboration of all involved components, and also greatly simplifies the classical issues arising from dynamic changes, such as consistency preserving etc. To demonstrate the feasibility of RBW, I created a dynamic secure middleware system - the Smart Data Server Version 3.0 (SDS3). In SDS3, an open source implementation of CORBA is adopted and modified as the communication infrastructure, and three secure components managed by RBW, are created to enhance the security on the access of deployed applications. SDS3 offers multi-level security control on its applications from strategy control to application-specific detail control. For the management by RBW, the strategy control of SDS3 applications could be dynamically changed by reorganizing the collaboration of the three secure components. In addition, I created the Dynamic Services Composer (DSC) based on Apache open source projects, Apache Axis and WSIF. In DSC, RBW is employed to model the interaction and collaboration of web services and to enable the dynamic changes on the flow structure of web services. Finally, overall performance tests were made to evaluate the efficiency of the developed RBW and SDS3. The results demonstrated that temporary binding of component instances makes slight impacts on the execution efficiency of components, and the blackout time arising from dynamic changes can be extremely reduced in any applications.
Tangible business process modeling : design and evaluation of a process model elicitation Technique
(2011)
Structuring process models
(2012)
One can fairly adopt the ideas of Donald E. Knuth to conclude that process modeling is both a science and an art. Process modeling does have an aesthetic sense. Similar to composing an opera or writing a novel, process modeling is carried out by humans who undergo creative practices when engineering a process model. Therefore, the very same process can be modeled in a myriad number of ways. Once modeled, processes can be analyzed by employing scientific methods. Usually, process models are formalized as directed graphs, with nodes representing tasks and decisions, and directed arcs describing temporal constraints between the nodes. Common process definition languages, such as Business Process Model and Notation (BPMN) and Event-driven Process Chain (EPC) allow process analysts to define models with arbitrary complex topologies. The absence of structural constraints supports creativity and productivity, as there is no need to force ideas into a limited amount of available structural patterns. Nevertheless, it is often preferable that models follow certain structural rules. A well-known structural property of process models is (well-)structuredness. A process model is (well-)structured if and only if every node with multiple outgoing arcs (a split) has a corresponding node with multiple incoming arcs (a join), and vice versa, such that the set of nodes between the split and the join induces a single-entry-single-exit (SESE) region; otherwise the process model is unstructured. The motivations for well-structured process models are manifold: (i) Well-structured process models are easier to layout for visual representation as their formalizations are planar graphs. (ii) Well-structured process models are easier to comprehend by humans. (iii) Well-structured process models tend to have fewer errors than unstructured ones and it is less probable to introduce new errors when modifying a well-structured process model. (iv) Well-structured process models are better suited for analysis with many existing formal techniques applicable only for well-structured process models. (v) Well-structured process models are better suited for efficient execution and optimization, e.g., when discovering independent regions of a process model that can be executed concurrently. Consequently, there are process modeling languages that encourage well-structured modeling, e.g., Business Process Execution Language (BPEL) and ADEPT. However, the well-structured process modeling implies some limitations: (i) There exist processes that cannot be formalized as well-structured process models. (ii) There exist processes that when formalized as well-structured process models require a considerable duplication of modeling constructs. Rather than expecting well-structured modeling from start, we advocate for the absence of structural constraints when modeling. Afterwards, automated methods can suggest, upon request and whenever possible, alternative formalizations that are "better" structured, preferably well-structured. In this thesis, we study the problem of automatically transforming process models into equivalent well-structured models. The developed transformations are performed under a strong notion of behavioral equivalence which preserves concurrency. The findings are implemented in a tool, which is publicly available.
In the early days of computer graphics, research was mainly driven by the goal to create realistic synthetic imagery. By contrast, non-photorealistic computer graphics, established as its own branch of computer graphics in the early 1990s, is mainly motivated by concepts and principles found in traditional art forms, such as painting, illustration, and graphic design, and it investigates concepts and techniques that abstract from reality using expressive, stylized, or illustrative rendering techniques. This thesis focuses on the artistic stylization of two-dimensional content and presents several novel automatic techniques for the creation of simplified stylistic illustrations from color images, video, and 3D renderings. Primary innovation of these novel techniques is that they utilize the smooth structure tensor as a simple and efficient way to obtain information about the local structure of an image. More specifically, this thesis contributes to knowledge in this field in the following ways. First, a comprehensive review of the structure tensor is provided. In particular, different methods for integrating the minor eigenvector field of the smoothed structure tensor are developed, and the superiority of the smoothed structure tensor over the popular edge tangent flow is demonstrated. Second, separable implementations of the popular bilateral and difference of Gaussians filters that adapt to the local structure are presented. These filters avoid artifacts while being computationally highly efficient. Taken together, both provide an effective way to create a cartoon-style effect. Third, a generalization of the Kuwahara filter is presented that avoids artifacts by adapting the shape, scale, and orientation of the filter to the local structure. This causes directional image features to be better preserved and emphasized, resulting in overall sharper edges and a more feature-abiding painterly effect. In addition to the single-scale variant, a multi-scale variant is presented, which is capable of performing a highly aggressive abstraction. Fourth, a technique that builds upon the idea of combining flow-guided smoothing with shock filtering is presented, allowing for an aggressive exaggeration and an emphasis of directional image features. All presented techniques are suitable for temporally coherent per-frame filtering of video or dynamic 3D renderings, without requiring expensive extra processing, such as optical flow. Moreover, they can be efficiently implemented to process content in real-time on a GPU.
This work introduces novel internal and external memory algorithms for computing voxel skeletons of massive voxel objects with complex network-like architecture and for converting these voxel skeletons to piecewise linear geometry, that is triangle meshes and piecewise straight lines. The presented techniques help to tackle the challenge of visualizing and analyzing 3d images of increasing size and complexity, which are becoming more and more important in, for example, biological and medical research. Section 2.3.1 contributes to the theoretical foundations of thinning algorithms with a discussion of homotopic thinning in the grid cell model. The grid cell model explicitly represents a cell complex built of faces, edges, and vertices shared between voxels. A characterization of pairs of cells to be deleted is much simpler than characterizations of simple voxels were before. The grid cell model resolves topologically unclear voxel configurations at junctions and locked voxel configurations causing, for example, interior voxels in sets of non-simple voxels. A general conclusion is that the grid cell model is superior to indecomposable voxels for algorithms that need detailed control of topology. Section 2.3.2 introduces a noise-insensitive measure based on the geodesic distance along the boundary to compute two-dimensional skeletons. The measure is able to retain thin object structures if they are geometrically important while ignoring noise on the object's boundary. This combination of properties is not known of other measures. The measure is also used to guide erosion in a thinning process from the boundary towards lines centered within plate-like structures. Geodesic distance based quantities seem to be well suited to robustly identify one- and two-dimensional skeletons. Chapter 6 applies the method to visualization of bone micro-architecture. Chapter 3 describes a novel geometry generation scheme for representing voxel skeletons, which retracts voxel skeletons to piecewise linear geometry per dual cube. The generated triangle meshes and graphs provide a link to geometry processing and efficient rendering of voxel skeletons. The scheme creates non-closed surfaces with boundaries, which contain fewer triangles than a representation of voxel skeletons using closed surfaces like small cubes or iso-surfaces. A conclusion is that thinking specifically about voxel skeleton configurations instead of generic voxel configurations helps to deal with the topological implications. The geometry generation is one foundation of the applications presented in Chapter 6. Chapter 5 presents a novel external memory algorithm for distance ordered homotopic thinning. The presented method extends known algorithms for computing chamfer distance transformations and thinning to execute I/O-efficiently when input is larger than the available main memory. The applied block-wise decomposition schemes are quite simple. Yet it was necessary to carefully analyze effects of block boundaries to devise globally correct external memory variants of known algorithms. In general, doing so is superior to naive block-wise processing ignoring boundary effects. Chapter 6 applies the algorithms in a novel method based on confocal microscopy for quantitative study of micro-vascular networks in the field of microcirculation.
Simulationskonzept zur Nutzenvalidierung cyber-physischer Systeme in komplexen Fabrikumgebungen
(2017)
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.
Software-as-a-Service (SaaS) offers several advantages to both service providers and users. Service providers can benefit from the reduction of Total Cost of Ownership (TCO), better scalability, and better resource utilization. On the other hand, users can use the service anywhere and anytime, and minimize upfront investment by following the pay-as-you-go model. Despite the benefits of SaaS, users still have concerns about the security and privacy of their data. Due to the nature of SaaS and the Cloud in general, the data and the computation are beyond the users' control, and hence data security becomes a vital factor in this new paradigm. Furthermore, in multi-tenant SaaS applications, the tenants become more concerned about the confidentiality of their data since several tenants are co-located onto a shared infrastructure.
To address those concerns, we start protecting the data from the provisioning process by controlling how tenants are being placed in the infrastructure. We present a resource allocation algorithm designed to minimize the risk of co-resident tenants called SecPlace. It enables the SaaS provider to control the resource (i.e., database instance) allocation process while taking into account the security of tenants as a requirement.
Due to the design principles of the multi-tenancy model, tenants follow some degree of sharing on both application and infrastructure levels. Thus, strong security-isolation should be present. Therefore, we develop SignedQuery, a technique that prevents one tenant from accessing others' data. We use the Signing Concept to create a signature that is used to sign the tenant's request, then the server can verifies the signature and recognizes the requesting tenant, and hence ensures that the data to be accessed is belonging to the legitimate tenant.
Finally, Data confidentiality remains a critical concern due to the fact that data in the Cloud is out of users' premises, and hence beyond their control. Cryptography is increasingly proposed as a potential approach to address such a challenge. Therefore, we present SecureDB, a system designed to run SQL-based applications over an encrypted database. SecureDB captures the schema design and analyzes it to understand the internal structure of the data (i.e., relationships between the tables and their attributes). Moreover, we determine the appropriate partialhomomorphic encryption scheme for each attribute where computation is possible even when the data is encrypted.
To evaluate our work, we conduct extensive experiments with di↵erent settings. The main use case in our work is a popular open source HRM application, called OrangeHRM. The results show that our multi-layered approach is practical, provides enhanced security and isolation among tenants, and have a moderate complexity in terms of processing encrypted data.
Cloud computing is a model for enabling on-demand access to a shared pool of computing resources. With virtually limitless on-demand resources, a cloud environment enables the hosted Internet application to quickly cope when there is an increase in the workload. However, the overhead of provisioning resources exposes the Internet application to periods of under-provisioning and performance degradation. Moreover, the performance interference, due to the consolidation in the cloud environment, complicates the performance management of the Internet applications. In this dissertation, we propose two approaches to mitigate the impact of the resources provisioning overhead. The first approach employs control theory to scale resources vertically and cope fast with workload. This approach assumes that the provider has knowledge and control over the platform running in the virtual machines (VMs), which limits it to Platform as a Service (PaaS) and Software as a Service (SaaS) providers. The second approach is a customer-side one that deals with the horizontal scalability in an Infrastructure as a Service (IaaS) model. It addresses the trade-off problem between cost and performance with a multi-goal optimization solution. This approach finds the scale thresholds that achieve the highest performance with the lowest increase in the cost. Moreover, the second approach employs a proposed time series forecasting algorithm to scale the application proactively and avoid under-utilization periods. Furthermore, to mitigate the interference impact on the Internet application performance, we developed a system which finds and eliminates the VMs suffering from performance interference. The developed system is a light-weight solution which does not imply provider involvement. To evaluate our approaches and the designed algorithms at large-scale level, we developed a simulator called (ScaleSim). In the simulator, we implemented scalability components acting as the scalability components of Amazon EC2. The current scalability implementation in Amazon EC2 is used as a reference point for evaluating the improvement in the scalable application performance. ScaleSim is fed with realistic models of the RUBiS benchmark extracted from the real environment. The workload is generated from the access logs of the 1998 world cup website. The results show that optimizing the scalability thresholds and adopting proactive scalability can mitigate 88% of the resources provisioning overhead impact with only a 9% increase in the cost.
In this work we consider statistical learning problems. A learning machine aims to extract information from a set of training examples such that it is able to predict the associated label on unseen examples. We consider the case where the resulting classification or regression rule is a combination of simple rules - also called base hypotheses. The so-called boosting algorithms iteratively find a weighted linear combination of base hypotheses that predict well on unseen data. We address the following issues: o The statistical learning theory framework for analyzing boosting methods. We study learning theoretic guarantees on the prediction performance on unseen examples. Recently, large margin classification techniques emerged as a practical result of the theory of generalization, in particular Boosting and Support Vector Machines. A large margin implies a good generalization performance. Hence, we analyze how large the margins in boosting are and find an improved algorithm that is able to generate the maximum margin solution. o How can boosting methods be related to mathematical optimization techniques? To analyze the properties of the resulting classification or regression rule, it is of high importance to understand whether and under which conditions boosting converges. We show that boosting can be used to solve large scale constrained optimization problems, whose solutions are well characterizable. To show this, we relate boosting methods to methods known from mathematical optimization, and derive convergence guarantees for a quite general family of boosting algorithms. o How to make Boosting noise robust? One of the problems of current boosting techniques is that they are sensitive to noise in the training sample. In order to make boosting robust, we transfer the soft margin idea from support vector learning to boosting. We develop theoretically motivated regularized algorithms that exhibit a high noise robustness. o How to adapt boosting to regression problems? Boosting methods are originally designed for classification problems. To extend the boosting idea to regression problems, we use the previous convergence results and relations to semi-infinite programming to design boosting-like algorithms for regression problems. We show that these leveraging algorithms have desirable theoretical and practical properties. o Can boosting techniques be useful in practice? The presented theoretical results are guided by simulation results either to illustrate properties of the proposed algorithms or to show that they work well in practice. We report on successful applications in a non-intrusive power monitoring system, chaotic time series analysis and a drug discovery process. --- Anmerkung: Der Autor ist Träger des von der Mathematisch-Naturwissenschaftlichen Fakultät der Universität Potsdam vergebenen Michelson-Preises für die beste Promotion des Jahres 2001/2002.
The constantly growing capacity of reconfigurable devices allows simultaneous execution of complex applications on those devices. The mere diversity of applications deems it impossible to design an interconnection network matching the requirements of every possible application perfectly, leading to suboptimal performance in many cases. However, the architecture of the interconnection network is not the only aspect affecting performance of communication. The resource manager places applications on the device and therefore influences latency between communicating partners and overall network load. Communication protocols affect performance by introducing data and processing overhead putting higher load on the network and increasing resource demand. Approaching communication holistically not only considers the architecture of the interconnect, but communication-aware resource management, communication protocols and resource usage just as well. Incorporation of different parts of a reconfigurable system during design- and runtime and optimizing them with respect to communication demand results in more resource efficient communication. Extensive evaluation shows enhanced performance and flexibility, if communication on reconfigurable devices is regarded in a holistic fashion.
Deciphering the functioning of biological networks is one of the central tasks in systems biology. In particular, signal transduction networks are crucial for the understanding of the cellular response to external and internal perturbations. Importantly, in order to cope with the complexity of these networks, mathematical and computational modeling is required. We propose a computational modeling framework in order to achieve more robust discoveries in the context of logical signaling networks. More precisely, we focus on modeling the response of logical signaling networks by means of automated reasoning using Answer Set Programming (ASP). ASP provides a declarative language for modeling various knowledge representation and reasoning problems. Moreover, available ASP solvers provide several reasoning modes for assessing the multitude of answer sets. Therefore, leveraging its rich modeling language and its highly efficient solving capacities, we use ASP to address three challenging problems in the context of logical signaling networks: learning of (Boolean) logical networks, experimental design, and identification of intervention strategies. Overall, the contribution of this thesis is three-fold. Firstly, we introduce a mathematical framework for characterizing and reasoning on the response of logical signaling networks. Secondly, we contribute to a growing list of successful applications of ASP in systems biology. Thirdly, we present a software providing a complete pipeline for automated reasoning on the response of logical signaling networks.
Real-Time-Non-Photorealistic rendering techniques for illustrating 3D scenes and their dynamics
(2005)
An increasing number of applications requires user interfaces that facilitate the handling of large geodata sets. Using virtual 3D city models, complex geospatial information can be communicated visually in an intuitive way. Therefore, real-time visualization of virtual 3D city models represents a key functionality for interactive exploration, presentation, analysis, and manipulation of geospatial data. This thesis concentrates on the development and implementation of concepts and techniques for real-time city model visualization. It discusses rendering algorithms as well as complementary modeling concepts and interaction techniques. Particularly, the work introduces a new real-time rendering technique to handle city models of high complexity concerning texture size and number of textures. Such models are difficult to handle by current technology, primarily due to two problems: - Limited texture memory: The amount of simultaneously usable texture data is limited by the memory of the graphics hardware. - Limited number of textures: Using several thousand different textures simultaneously causes significant performance problems due to texture switch operations during rendering. The multiresolution texture atlases approach, introduced in this thesis, overcomes both problems. During rendering, it permanently maintains a small set of textures that are sufficient for the current view and the screen resolution available. The efficiency of multiresolution texture atlases is evaluated in performance tests. To summarize, the results demonstrate that the following goals have been achieved: - Real-time rendering becomes possible for 3D scenes whose amount of texture data exceeds the main memory capacity. - Overhead due to texture switches is kept permanently low, so that the number of different textures has no significant effect on the rendering frame rate. Furthermore, this thesis introduces two new approaches for real-time city model visualization that use textures as core visualization elements: - An approach for visualization of thematic information. - An approach for illustrative visualization of 3D city models. Both techniques demonstrate that multiresolution texture atlases provide a basic functionality for the development of new applications and systems in the domain of city model visualization.
The modeling and evaluation calculus FMC-QE, the Fundamental Modeling Concepts for Quanti-tative Evaluation [1], extends the Fundamental Modeling Concepts (FMC) for performance modeling and prediction. In this new methodology, the hierarchical service requests are in the main focus, because they are the origin of every service provisioning process. Similar to physics, these service requests are a tuple of value and unit, which enables hierarchical service request transformations at the hierarchical borders and therefore the hierarchical modeling. Through reducing the model complexity of the models by decomposing the system in different hierarchical views, the distinction between operational and control states and the calculation of the performance values on the assumption of the steady state, FMC-QE has a scalable applica-bility on complex systems. According to FMC, the system is modeled in a 3-dimensional hierarchical representation space, where system performance parameters are described in three arbitrarily fine-grained hierarchi-cal bipartite diagrams. The hierarchical service request structures are modeled in Entity Relationship Diagrams. The static server structures, divided into logical and real servers, are de-scribed as Block Diagrams. The dynamic behavior and the control structures are specified as Petri Nets, more precisely Colored Time Augmented Petri Nets. From the structures and pa-rameters of the performance model, a hierarchical set of equations is derived. The calculation of the performance values is done on the assumption of stationary processes and is based on fundamental laws of the performance analysis: Little's Law and the Forced Traffic Flow Law. Little's Law is used within the different hierarchical levels (horizontal) and the Forced Traffic Flow Law is the key to the dependencies among the hierarchical levels (vertical). This calculation is suitable for complex models and allows a fast (re-)calculation of different performance scenarios in order to support development and configuration decisions. Within the Research Group Zorn at the Hasso Plattner Institute, the work is embedded in a broader research in the development of FMC-QE. While this work is concentrated on the theoretical background, description and definition of the methodology as well as the extension and validation of the applicability, other topics are in the development of an FMC-QE modeling and evaluation tool and the usage of FMC-QE in the design of an adaptive transport layer in order to fulfill Quality of Service and Service Level Agreements in volatile service based environments. This thesis contains a state-of-the-art, the description of FMC-QE as well as extensions of FMC-QE in representative general models and case studies. In the state-of-the-art part of the thesis in chapter 2, an overview on existing Queueing Theory and Time Augmented Petri Net models and other quantitative modeling and evaluation languages and methodologies is given. Also other hierarchical quantitative modeling frameworks will be considered. The description of FMC-QE in chapter 3 consists of a summary of the foundations of FMC-QE, basic definitions, the graphical notations, the FMC-QE Calculus and the modeling of open queueing networks as an introductory example. The extensions of FMC-QE in chapter 4 consist of the integration of the summation method in order to support the handling of closed networks and the modeling of multiclass and semaphore scenarios. Furthermore, FMC-QE is compared to other performance modeling and evaluation approaches. In the case study part in chapter 5, proof-of-concept examples, like the modeling of a service based search portal, a service based SAP NetWeaver application and the Axis2 Web service framework will be provided. Finally, conclusions are given by a summary of contributions and an outlook on future work in chapter 6. [1] Werner Zorn. FMC-QE - A New Approach in Quantitative Modeling. In Hamid R. Arabnia, editor, Procee-dings of the International Conference on Modeling, Simulation and Visualization Methods (MSV 2007) within WorldComp ’07, pages 280 – 287, Las Vegas, NV, USA, June 2007. CSREA Press. ISBN 1-60132-029-9.
Answer Set Programming (ASP) is an emerging paradigm for declarative programming, in which a computational problem is specified by a logic program such that particular models, called answer sets, match solutions. ASP faces a growing range of applications, demanding for high-performance tools able to solve complex problems. ASP integrates ideas from a variety of neighboring fields. In particular, automated techniques to search for answer sets are inspired by Boolean Satisfiability (SAT) solving approaches. While the latter have firm proof-theoretic foundations, ASP lacks formal frameworks for characterizing and comparing solving methods. Furthermore, sophisticated search patterns of modern SAT solvers, successfully applied in areas like, e.g., model checking and verification, are not yet established in ASP solving. We address these deficiencies by, for one, providing proof-theoretic frameworks that allow for characterizing, comparing, and analyzing approaches to answer set computation. For another, we devise modern ASP solving algorithms that integrate and extend state-of-the-art techniques for Boolean constraint solving. We thus contribute to the understanding of existing ASP solving approaches and their interconnections as well as to their enhancement by incorporating sophisticated search patterns. The central idea of our approach is to identify atomic as well as composite constituents of a propositional logic program with Boolean variables. This enables us to describe fundamental inference steps, and to selectively combine them in proof-theoretic characterizations of various ASP solving methods. In particular, we show that different concepts of case analyses applied by existing ASP solvers implicate mutual exponential separations regarding their best-case complexities. We also develop a generic proof-theoretic framework amenable to language extensions, and we point out that exponential separations can likewise be obtained due to case analyses on them. We further exploit fundamental inference steps to derive Boolean constraints characterizing answer sets. They enable the conception of ASP solving algorithms including search patterns of modern SAT solvers, while also allowing for direct technology transfers between the areas of ASP and SAT solving. Beyond the search for one answer set of a logic program, we address the enumeration of answer sets and their projections to a subvocabulary, respectively. The algorithms we develop enable repetition-free enumeration in polynomial space without being intrusive, i.e., they do not necessitate any modifications of computations before an answer set is found. Our approach to ASP solving is implemented in clasp, a state-of-the-art Boolean constraint solver that has successfully participated in recent solver competitions. Although we do here not address the implementation techniques of clasp or all of its features, we present the principles of its success in the context of ASP solving.
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.
This thesis proposes a privacy protection framework for the controlled distribution and use of personal private data. The framework is based on the idea that privacy policies can be set directly by the data owner and can be automatically enforced against the data user. Data privacy continues to be a very important topic, as our dependency on electronic communication maintains its current growth, and private data is shared between multiple devices, users and locations. The growing amount and the ubiquitous availability of personal private data increases the likelihood of data misuse. Early privacy protection techniques, such as anonymous email and payment systems have focused on data avoidance and anonymous use of services. They did not take into account that data sharing cannot be avoided when people participate in electronic communication scenarios that involve social interactions. This leads to a situation where data is shared widely and uncontrollably and in most cases the data owner has no control over further distribution and use of personal private data. Previous efforts to integrate privacy awareness into data processing workflows have focused on the extension of existing access control frameworks with privacy aware functions or have analysed specific individual problems such as the expressiveness of policy languages. So far, very few implementations of integrated privacy protection mechanisms exist and can be studied to prove their effectiveness for privacy protection. Second level issues that stem from practical application of the implemented mechanisms, such as usability, life-time data management and changes in trustworthiness have received very little attention so far, mainly because they require actual implementations to be studied. Most existing privacy protection schemes silently assume that it is the privilege of the data user to define the contract under which personal private data is released. Such an approach simplifies policy management and policy enforcement for the data user, but leaves the data owner with a binary decision to submit or withhold his or her personal data based on the provided policy. We wanted to empower the data owner to express his or her privacy preferences through privacy policies that follow the so-called Owner-Retained Access Control (ORAC) model. ORAC has been proposed by McCollum, et al. as an alternate access control mechanism that leaves the authority over access decisions by the originator of the data. The data owner is given control over the release policy for his or her personal data, and he or she can set permissions or restrictions according to individually perceived trust values. Such a policy needs to be expressed in a coherent way and must allow the deterministic policy evaluation by different entities. The privacy policy also needs to be communicated from the data owner to the data user, so that it can be enforced. Data and policy are stored together as a Protected Data Object that follows the Sticky Policy paradigm as defined by Mont, et al. and others. We developed a unique policy combination approach that takes usability aspects for the creation and maintenance of policies into consideration. Our privacy policy consists of three parts: A Default Policy provides basic privacy protection if no specific rules have been entered by the data owner. An Owner Policy part allows the customisation of the default policy by the data owner. And a so-called Safety Policy guarantees that the data owner cannot specify disadvantageous policies, which, for example, exclude him or her from further access to the private data. The combined evaluation of these three policy-parts yields the necessary access decision. The automatic enforcement of privacy policies in our protection framework is supported by a reference monitor implementation. We started our work with the development of a client-side protection mechanism that allows the enforcement of data-use restrictions after private data has been released to the data user. The client-side enforcement component for data-use policies is based on a modified Java Security Framework. Privacy policies are translated into corresponding Java permissions that can be automatically enforced by the Java Security Manager. When we later extended our work to implement server-side protection mechanisms, we found several drawbacks for the privacy enforcement through the Java Security Framework. We solved this problem by extending our reference monitor design to use Aspect-Oriented Programming (AOP) and the Java Reflection API to intercept data accesses in existing applications and provide a way to enforce data owner-defined privacy policies for business applications.
Answer Set Programming (ASP) emerged in the late 1990s as a new logic programming paradigm, having its roots in nonmonotonic reasoning, deductive databases, and logic programming with negation as failure. The basic idea of ASP is to represent a computational problem as a logic program whose answer sets correspond to solutions, and then to use an answer set solver for finding answer sets of the program. ASP is particularly suited for solving NP-complete search problems. Among these, we find applications to product configuration, diagnosis, and graph-theoretical problems, e.g. finding Hamiltonian cycles. On different lines of ASP research, many extensions of the basic formalism have been proposed. The most intensively studied one is the modelling of preferences in ASP. They constitute a natural and effective way of selecting preferred solutions among a plethora of solutions for a problem. For example, preferences have been successfully used for timetabling, auctioning, and product configuration. In this thesis, we concentrate on preferences within answer set programming. Among several formalisms and semantics for preference handling in ASP, we concentrate on ordered logic programs with the underlying D-, W-, and B-semantics. In this setting, preferences are defined among rules of a logic program. They select preferred answer sets among (standard) answer sets of the underlying logic program. Up to now, those preferred answer sets have been computed either via a compilation method or by meta-interpretation. Hence, the question comes up, whether and how preferences can be integrated into an existing ASP solver. To solve this question, we develop an operational graph-based framework for the computation of answer sets of logic programs. Then, we integrate preferences into this operational approach. We empirically observe that our integrative approach performs in most cases better than the compilation method or meta-interpretation. Another research issue in ASP are optimization methods that remove redundancies, as also found in database query optimizers. For these purposes, the rather recently suggested notion of strong equivalence for ASP can be used. If a program is strongly equivalent to a subprogram of itself, then one can always use the subprogram instead of the original program, a technique which serves as an effective optimization method. Up to now, strong equivalence has not been considered for logic programs with preferences. In this thesis, we tackle this issue and generalize the notion of strong equivalence to ordered logic programs. We give necessary and sufficient conditions for the strong equivalence of two ordered logic programs. Furthermore, we provide program transformations for ordered logic programs and show in how far preferences can be simplified. Finally, we present two new applications for preferences within answer set programming. First, we define new procedures for group decision making, which we apply to the problem of scheduling a group meeting. As a second new application, we reconstruct a linguistic problem appearing in German dialects within ASP. Regarding linguistic studies, there is an ongoing debate about how unique the rule systems of language are in human cognition. The reconstruction of grammatical regularities with tools from computer science has consequences for this debate: if grammars can be modelled this way, then they share core properties with other non-linguistic rule systems.
Learning a model for the relationship between the attributes and the annotated labels of data examples serves two purposes. Firstly, it enables the prediction of the label for examples without annotation. Secondly, the parameters of the model can provide useful insights into the structure of the data. If the data has an inherent partitioned structure, it is natural to mirror this structure in the model. Such mixture models predict by combining the individual predictions generated by the mixture components which correspond to the partitions in the data. Often the partitioned structure is latent, and has to be inferred when learning the mixture model. Directly evaluating the accuracy of the inferred partition structure is, in many cases, impossible because the ground truth cannot be obtained for comparison. However it can be assessed indirectly by measuring the prediction accuracy of the mixture model that arises from it. This thesis addresses the interplay between the improvement of predictive accuracy by uncovering latent cluster structure in data, and further addresses the validation of the estimated structure by measuring the accuracy of the resulting predictive model. In the application of filtering unsolicited emails, the emails in the training set are latently clustered into advertisement campaigns. Uncovering this latent structure allows filtering of future emails with very low false positive rates. In order to model the cluster structure, a Bayesian clustering model for dependent binary features is developed in this thesis. Knowing the clustering of emails into campaigns can also aid in uncovering which emails have been sent on behalf of the same network of captured hosts, so-called botnets. This association of emails to networks is another layer of latent clustering. Uncovering this latent structure allows service providers to further increase the accuracy of email filtering and to effectively defend against distributed denial-of-service attacks. To this end, a discriminative clustering model is derived in this thesis that is based on the graph of observed emails. The partitionings inferred using this model are evaluated through their capacity to predict the campaigns of new emails. Furthermore, when classifying the content of emails, statistical information about the sending server can be valuable. Learning a model that is able to make use of it requires training data that includes server statistics. In order to also use training data where the server statistics are missing, a model that is a mixture over potentially all substitutions thereof is developed. Another application is to predict the navigation behavior of the users of a website. Here, there is no a priori partitioning of the users into clusters, but to understand different usage scenarios and design different layouts for them, imposing a partitioning is necessary. The presented approach simultaneously optimizes the discriminative as well as the predictive power of the clusters. Each model is evaluated on real-world data and compared to baseline methods. The results show that explicitly modeling the assumptions about the latent cluster structure leads to improved predictions compared to the baselines. It is beneficial to incorporate a small number of hyperparameters that can be tuned to yield the best predictions in cases where the prediction accuracy can not be optimized directly.
In many applications one is faced with the problem of inferring some functional relation between input and output variables from given data. Consider, for instance, the task of email spam filtering where one seeks to find a model which automatically assigns new, previously unseen emails to class spam or non-spam. Building such a predictive model based on observed training inputs (e.g., emails) with corresponding outputs (e.g., spam labels) is a major goal of machine learning. Many learning methods assume that these training data are governed by the same distribution as the test data which the predictive model will be exposed to at application time. That assumption is violated when the test data are generated in response to the presence of a predictive model. This becomes apparent, for instance, in the above example of email spam filtering. Here, email service providers employ spam filters and spam senders engineer campaign templates such as to achieve a high rate of successful deliveries despite any filters. Most of the existing work casts such situations as learning robust models which are unsusceptible against small changes of the data generation process. The models are constructed under the worst-case assumption that these changes are performed such to produce the highest possible adverse effect on the performance of the predictive model. However, this approach is not capable to realistically model the true dependency between the model-building process and the process of generating future data. We therefore establish the concept of prediction games: We model the interaction between a learner, who builds the predictive model, and a data generator, who controls the process of data generation, as an one-shot game. The game-theoretic framework enables us to explicitly model the players' interests, their possible actions, their level of knowledge about each other, and the order at which they decide for an action. We model the players' interests as minimizing their own cost function which both depend on both players' actions. The learner's action is to choose the model parameters and the data generator's action is to perturbate the training data which reflects the modification of the data generation process with respect to the past data. We extensively study three instances of prediction games which differ regarding the order in which the players decide for their action. We first assume that both player choose their actions simultaneously, that is, without the knowledge of their opponent's decision. We identify conditions under which this Nash prediction game has a meaningful solution, that is, a unique Nash equilibrium, and derive algorithms that find the equilibrial prediction model. As a second case, we consider a data generator who is potentially fully informed about the move of the learner. This setting establishes a Stackelberg competition. We derive a relaxed optimization criterion to determine the solution of this game and show that this Stackelberg prediction game generalizes existing prediction models. Finally, we study the setting where the learner observes the data generator's action, that is, the (unlabeled) test data, before building the predictive model. As the test data and the training data may be governed by differing probability distributions, this scenario reduces to learning under covariate shift. We derive a new integrated as well as a two-stage method to account for this data set shift. In case studies on email spam filtering we empirically explore properties of all derived models as well as several existing baseline methods. We show that spam filters resulting from the Nash prediction game as well as the Stackelberg prediction game in the majority of cases outperform other existing baseline methods.
The objective and motivation behind this research is to provide applications with easy-to-use interfaces to communities of deaf and functionally illiterate users, which enables them to work without any human assistance. Although recent years have witnessed technological advancements, the availability of technology does not ensure accessibility to information and communication technologies (ICT). Extensive use of text from menus to document contents means that deaf or functionally illiterate can not access services implemented on most computer software. Consequently, most existing computer applications pose an accessibility barrier to those who are unable to read fluently. Online technologies intended for such groups should be developed in continuous partnership with primary users and include a thorough investigation into their limitations, requirements and usability barriers. In this research, I investigated existing tools in voice, web and other multimedia technologies to identify learning gaps and explored ways to enhance the information literacy for deaf and functionally illiterate users. I worked on the development of user-centered interfaces to increase the capabilities of deaf and low literacy users by enhancing lexical resources and by evaluating several multimedia interfaces for them. The interface of the platform-independent Italian Sign Language (LIS) Dictionary has been developed to enhance the lexical resources for deaf users. The Sign Language Dictionary accepts Italian lemmas as input and provides their representation in the Italian Sign Language as output. The Sign Language dictionary has 3082 signs as set of Avatar animations in which each sign is linked to a corresponding Italian lemma. I integrated the LIS lexical resources with MultiWordNet (MWN) database to form the first LIS MultiWordNet(LMWN). LMWN contains information about lexical relations between words, semantic relations between lexical concepts (synsets), correspondences between Italian and sign language lexical concepts and semantic fields (domains). The approach enhances the deaf users’ understanding of written Italian language and shows that a relatively small set of lexicon can cover a significant portion of MWN. Integration of LIS signs with MWN made it useful tool for computational linguistics and natural language processing. The rule-based translation process from written Italian text to LIS has been transformed into service-oriented system. The translation process is composed of various modules including parser, semantic interpreter, generator, and spatial allocation planner. This translation procedure has been implemented in the Java Application Building Center (jABC), which is a framework for extreme model driven design (XMDD). The XMDD approach focuses on bringing software development closer to conceptual design, so that the functionality of a software solution could be understood by someone who is unfamiliar with programming concepts. The transformation addresses the heterogeneity challenge and enhances the re-usability of the system. For enhancing the e-participation of functionally illiterate users, two detailed studies were conducted in the Republic of Rwanda. In the first study, the traditional (textual) interface was compared with the virtual character-based interactive interface. The study helped to identify usability barriers and users evaluated these interfaces according to three fundamental areas of usability, i.e. effectiveness, efficiency and satisfaction. In another study, we developed four different interfaces to analyze the usability and effects of online assistance (consistent help) for functionally illiterate users and compared different help modes including textual, vocal and virtual character on the performance of semi-literate users. In our newly designed interfaces the instructions were automatically translated in Swahili language. All the interfaces were evaluated on the basis of task accomplishment, time consumption, System Usability Scale (SUS) rating and number of times the help was acquired. The results show that the performance of semi-literate users improved significantly when using the online assistance. The dissertation thus introduces a new development approach in which virtual characters are used as additional support for barely literate or naturally challenged users. Such components enhanced the application utility by offering a variety of services like translating contents in local language, providing additional vocal information, and performing automatic translation from text to sign language. Obviously, there is no such thing as one design solution that fits for all in the underlying domain. Context sensitivity, literacy and mental abilities are key factors on which I concentrated and the results emphasize that computer interfaces must be based on a thoughtful definition of target groups, purposes and objectives.
Computer Security deals with the detection and mitigation of threats to computer networks, data, and computing hardware. This
thesis addresses the following two computer security problems: email spam campaign and malware detection.
Email spam campaigns can easily be generated using popular dissemination tools by specifying simple grammars that serve as message templates. A grammar is disseminated to nodes of a bot net, the nodes create messages by instantiating the grammar at random. Email spam campaigns can encompass huge data volumes and therefore pose a threat to the stability of the infrastructure of email service providers that have to store them. Malware -software that serves a malicious purpose- is affecting web servers, client computers via active content, and client computers through executable files. Without the help of malware detection systems it would be easy for malware creators to collect sensitive information or to infiltrate computers.
The detection of threats -such as email-spam messages, phishing messages, or malware- is an adversarial and therefore intrinsically
difficult problem. Threats vary greatly and evolve over time. The detection of threats based on manually-designed rules is therefore
difficult and requires a constant engineering effort. Machine-learning is a research area that revolves around the analysis of data and the discovery of patterns that describe aspects of the data. Discriminative learning methods extract prediction models from data that are optimized to predict a target attribute as accurately as possible. Machine-learning methods hold the promise of automatically identifying patterns that robustly and accurately detect threats. This thesis focuses on the design and analysis of discriminative learning methods for the two computer-security problems under investigation: email-campaign and malware detection.
The first part of this thesis addresses email-campaign detection. We focus on regular expressions as a syntactic framework, because regular expressions are intuitively comprehensible by security engineers and administrators, and they can be applied as a detection mechanism in an extremely efficient manner. In this setting, a prediction model is provided with exemplary messages from an email-spam campaign. The prediction model has to generate a regular expression that reveals the syntactic pattern that underlies the entire campaign, and that a security engineers finds comprehensible and feels confident enough to use the expression to blacklist further messages at the email server. We model this problem as two-stage learning problem with structured input and output spaces which can be solved using standard cutting plane methods. Therefore we develop an appropriate loss function, and derive a decoder for the resulting optimization problem.
The second part of this thesis deals with the problem of predicting whether a given JavaScript or PHP file is malicious or benign. Recent malware analysis techniques use static or dynamic features, or both. In fully dynamic analysis, the software or script is executed and observed for malicious behavior in a sandbox environment. By contrast, static analysis is based on features that can be extracted directly from the program file. In order to bypass static detection mechanisms, code obfuscation techniques are used to spread a malicious program file in many different syntactic variants. Deobfuscating the code before applying a static classifier can be subjected to mostly static code analysis and can overcome the problem of obfuscated malicious code, but on the other hand increases the computational costs of malware detection by an order of magnitude. In this thesis we present a cascaded architecture in which a classifier first performs a static analysis of the original code and -based on the outcome of this first classification step- the code may be deobfuscated and classified again. We explore several types of features including token $n$-grams, orthogonal sparse bigrams, subroutine-hashings, and syntax-tree features and study the robustness of detection methods and feature types against the evolution of malware over time. The developed tool scans very large file collections quickly and accurately.
Each model is evaluated on real-world data and compared to reference methods. Our approach of inferring regular expressions to filter emails belonging to an email spam campaigns leads to models with a high true-positive rate at a very low false-positive rate that is an order of magnitude lower than that of a commercial content-based filter. Our presented system -REx-SVMshort- is being used by a commercial email service provider and complements content-based and IP-address based filtering.
Our cascaded malware detection system is evaluated on a high-quality data set of almost 400,000 conspicuous PHP files and a collection of more than 1,00,000 JavaScript files. From our case study we can conclude that our system can quickly and accurately process large data collections at a low false-positive rate.
The objective of this thesis is to provide new space compaction techniques for testing or concurrent checking of digital circuits. In particular, the work focuses on the design of space compactors that achieve high compaction ratio and minimal loss of testability of the circuits. In the first part, the compactors are designed for combinational circuits based on the knowledge of the circuit structure. Several algorithms for analyzing circuit structures are introduced and discussed for the first time. The complexity of each design procedure is linear with respect to the number of gates of the circuit. Thus, the procedures are applicable to large circuits. In the second part, the first structural approach for output compaction for sequential circuits is introduced. Essentially, it enhances the first part. For the approach introduced in the third part it is assumed that the structure of the circuit and the underlying fault model are unknown. The space compaction approach requires only the knowledge of the fault-free test responses for a precomputed test set. The proposed compactor design guarantees zero-aliasing with respect to the precomputed test set.
Die Dissertation stellt eine neue Herangehensweise an die Lösung der Aufgabe der funktionalen Diagnostik digitaler Systeme vor. In dieser Arbeit wird eine neue Methode für die Fehlererkennung vorgeschlagen, basierend auf der Logischen Ergänzung und der Verwendung von Berger-Codes und dem 1-aus-3 Code. Die neue Fehlererkennungsmethode der Logischen Ergänzung gestattet einen hohen Optimierungsgrad der benötigten Realisationsfläche der konstruierten Fehlererkennungsschaltungen. Außerdem ist eins der wichtigen in dieser Dissertation gelösten Probleme die Synthese vollständig selbstprüfender Schaltungen.
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.