05 Fakultät Informatik, Elektrotechnik und Informationstechnik

Permanent URI for this collectionhttps://elib.uni-stuttgart.de/handle/11682/6

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Open Access
Task-oriented specialization techniques for entity retrieval
(2020) Glaser, Andrea; Kuhn, Jonas (Prof. Dr.)
Finding information on the internet has become very important nowadays, and online encyclopedias or websites specialized in certain topics offer users a great amount of information. Search engines support users when trying to find information. However, the vast amount of information makes it difficult to separate relevant from irrelevant facts for a specific information need. In this thesis we explore two areas of natural language processing in the context of retrieving information about entities: named entity disambiguation and sentiment analysis. The goal of this thesis is to use methods from these areas to develop task-oriented specialization techniques for entity retrieval. Named entity disambiguation is concerned with linking referring expressions (e.g., proper names) in text to their corresponding real world or fictional entity. Identifying the correct entity is an important factor in finding information on the internet as many proper names are ambiguous and need to be disambiguated to find relevant information. To that end, we introduce the notion of r-context, a new type of structurally informed context. This r-context consists of sentences that are relevant to the entity only to capture all important context clues and to avoid noise. We then show the usefulness of this r-context by performing a systematic study on a pseudo-ambiguity dataset. Identifying less known named entities is a challenge in named entity disambiguation because usually there is not much data available from which a machine learning algorithm can learn. We propose an approach that uses an aggregate of textual data about other entities which share certain properties with the target entity, and learn information from it by using topic modelling, which is then used to disambiguate the less known target entity. We use a dataset that is created automatically by exploiting the link structure in Wikipedia, and show that our approach is helpful for disambiguating entities without training material and with little surrounding context. Retrieving the relevant entities and information can produce many search results. Thus, it is important to effectively present the information to a user. We regard this step beyond the entity retrieval and employ sentiment analysis, which is used to analyze opinions expressed in text, in the context of effectively displaying information about product reviews to a user. We present a system that extracts a supporting sentence, a single sentence that captures both the sentiment of the author as well as a supportingfact. This supporting sentence can be used to provide users with an easy way to assess information in order to make informed choices quickly. We evaluate our approach by using the crowdsourcing service Amazon Mechanical Turk.
Open Access
Elastic parallel systems for high performance cloud computing
(2020) Kehrer, Stefan; Blochinger, Wolfgang (Prof. Dr.)
High Performance Computing (HPC) enables significant progress in both science and industry. Whereas traditionally parallel applications have been developed to address the grand challenges in science, as of today, they are also heavily used to speed up the time-to-result in the context of product design, production planning, financial risk management, medical diagnosis, as well as research and development efforts. However, purchasing and operating HPC clusters to run these applications requires huge capital expenditures as well as operational knowledge and thus is reserved to large organizations that benefit from economies of scale. More recently, the cloud evolved into an alternative execution environment for parallel applications, which comes with novel characteristics such as on-demand access to compute resources, pay-per-use, and elasticity. Whereas the cloud has been mainly used to operate interactive multi-tier applications, HPC users are also interested in the benefits offered. These include full control of the resource configuration based on virtualization, fast setup times by using on-demand accessible compute resources, and eliminated upfront capital expenditures due to the pay-per-use billing model. Additionally, elasticity allows compute resources to be provisioned and decommissioned at runtime, which allows fine-grained control of an application's performance in terms of its execution time and efficiency as well as the related monetary costs of the computation. Whereas HPC-optimized cloud environments have been introduced by cloud providers such as Amazon Web Services (AWS) and Microsoft Azure, existing parallel architectures are not designed to make use of elasticity. This thesis addresses several challenges in the emergent field of High Performance Cloud Computing. In particular, the presented contributions focus on the novel opportunities and challenges related to elasticity. First, the principles of elastic parallel systems as well as related design considerations are discussed in detail. On this basis, two exemplary elastic parallel system architectures are presented, each of which includes (1) an elasticity controller that controls the number of processing units based on user-defined goals, (2) a cloud-aware parallel execution model that handles coordination and synchronization requirements in an automated manner, and (3) a programming abstraction to ease the implementation of elastic parallel applications. To automate application delivery and deployment, novel approaches are presented that generate the required deployment artifacts from developer-provided source code in an automated manner while considering application-specific non-functional requirements. Throughout this thesis, a broad spectrum of design decisions related to the construction of elastic parallel system architectures is discussed, including proactive and reactive elasticity control mechanisms as well as cloud-based parallel processing with virtual machines (Infrastructure as a Service) and functions (Function as a Service). To evaluate these contributions, extensive experimental evaluations are presented.
Open Access
Data-efficient and safe learning with Gaussian processes
(2020) Schreiter, Jens; Toussaint, Marc (Prof. Dr. rer. nat.)
Data-based modeling techniques enjoy increasing popularity in many areas of science and technology where traditional approaches are limited regarding accuracy and efficiency. When employing machine learning methods to generate models of dynamic system, it is necessary to consider two important issues. Firstly, the data-sampling process should induce an informative and representative set of points to enable high generalization accuracy of the learned models. Secondly, the algorithmic part for efficient model building is essential for applicability, usability, and the quality of the learned predictive model. This thesis deals with both of these aspects for supervised learning problems, where the interaction between them is exploited to realize an exact and powerful modeling. After introducing the non-parametric Bayesian modeling approach with Gaussian processes and basics for transient modeling tasks in the next chapter, we dedicate ourselves to extensions of this probabilistic technique to relevant practical requirements in the subsequent chapter. This chapter provides an overview on existing sparse Gaussian process approximations and propose some novel work to increase efficiency and model selection on particularly large training data sets. For example, our sparse modeling approach enables real-time capable prediction performance and efficient learning with low memory requirements. A comprehensive comparison on various real-world problems confirms the proposed contributions and shows a variety of modeling tasks, where approximate Gaussian processes can be successfully applied. Further experiments provide more insight about the whole learning process, and thus a profound understanding of the presented work. In the fourth chapter, we focus on active learning schemes for safe and information-optimal generation of meaningful data sets. In addition to the exploration behavior of the active learner, the safety issue is considered in our work, since interacting with real systems should not result in damages or even completely destroy it. Here we propose a new model-based active learning framework to solve both tasks simultaneously. As basis for the data-sampling process we employ the presented Gaussian process techniques. Furthermore, we distinguish between static and transient experimental design strategies. Both problems are separately considered in this chapter. Nevertheless, the requirements for each active learning problem are the same. This subdivision into a static and transient setting allows a more problem-specific perspective on the two cases, and thus enables the creation of specially adapted active learning algorithms. Our novel approaches are then investigated for different applications, where a favorable trade-off between safety and exploration is always realized. Theoretical results maintain these evaluations and provide respectable knowledge about the derived model-based active learning schemes. For example, an upper bound for the probability of failure of the presented active learning methods is derived under reasonable assumptions. Finally, the thesis concludes with a summary of the investigated machine learning problems and motivate some future research directions.
Open Access
Automatic term extraction for conventional and extended term definitions across domains
(2020) Hätty, Anna; Schulte im Walde, Sabine (apl. Prof. Dr.)
A terminology is the entirety of concepts which constitute the vocabulary of a domain or subject field. Automatically identifying various linguistic forms of terms in domain-specific corpora is an important basis for further natural language processing tasks, such as ontology creation or, in general, domain knowledge acquisition. As a short overview for terms and domains, expressions like 'hammer', 'jigsaw', 'cordless screwdriver' or 'to drill' can be considered as terms in the domain of DIY (’do-it-yourself’); 'beaten egg whites' or 'electric blender' as terms in the domain of cooking. These examples cover different linguistic forms: simple terms like 'hammer' and complex terms like 'beaten egg whites', which consist of several simple words. However, although these words might seem to be obvious examples of terms, in many cases the decision to distinguish a term from a ‘non-term’ is not straightforward. There is no common, established way to define terms, but there are multiple terminology theories and diverse approaches to conduct human annotation studies. In addition, terms can be perceived to be more or less terminological, and the hard distinction between term and ‘non-term’ can be unsatisfying. Beyond term definition, when it comes to the automatic extraction of terms, there are further challenges, considering that complex terms as well as simple terms need to be automatically identified by an extraction system. The extraction of complex terms can profit from exploiting information about their constituents because complex terms might be infrequent as a whole. Simple terms might be more frequent, but they are especially prone to ambiguity. If a system considers an assumed term occurrence in text, which actually carries a different meaning, this can lead to wrong term extraction results. Thus, term complexity and ambiguity are major challenges for automatic term extraction. The present work describes novel theoretical and computational models for the considered aspects. It can be grouped into three broad categories: term definition studies, conventional automatic term extraction models, and extended automatic term extraction models that are based on fine-grained term frameworks. Term complexity and ambiguity are special foci here. In this thesis, we report on insights and improvements on these theoretical and computational models for terminology: We find that terms are concepts that can intuitively be derstood by lay people. We test more fine-grained term characterization frameworks that go beyond the conventional term/‘non-term’-distinction. We are the first to describe and model term ambiguity as gradual meaning variation between general and domain-specific language, and use the resulting representations to prevent errors typically made by term extraction systems resulting from ambiguity. We develop computational models that exploit the influence of term constituents on the prediction of complex terms. We especially tackle German closed compound terms, which are a frequent complex term type in German. Finally, we find that we can use similar strategies for modeling term complexity and ambiguity computationally for conventional and extended term extraction.
Open Access
Der Germanium-Zener-Emitter für die Silizium-Photonik
(2020) Körner, Roman; Schulze, Jörg (Prof. Dr. habil.)
Open Access
On consistency and distribution in software-defined networking
(2020) Kohler, Thomas; Rothermel, Kurt (Prof. Dr. rer. nat. Dr. h. c.)
Software-defined Networking (SDN) is an emerging networking paradigm promising flexible programmability and simplified management. Over the last years, SDN has built up huge momentum in academia that has led to huge practical impact through the large-scale adoption of big industrial players like Google, Facebook, and Microsoft driving cloud computing, data center networks, and their interconnection in SDN-based wide-area networks. SDN is a key enabler for high dynamics in terms of network reconfiguration and innovation, allowing the deployment of new network protocols and substantially expanding the networking paradigm by moving applications into the network, both at unprecedented pace and ease. The SDN paradigm is centered around the separation of the data plane from the logically centralized but typically physically distributed control plane that programs the forwarding behaviour of the network devices in the data plane based on a global view. Especially requirements on correctness, scalability, availability, and resiliency raised through practical adoption at scale have put a strong emphasis on consistency and distribution in the SDN paradigm. This thesis addresses various challenges regarding consistency and distribution in Software-defined Networking. More specifically, it focusses and contributes to the research areas of update consistency, flexibility in control plane distribution, and data plane implementation of a distributed application. Reconfiguring an SDN-based network inevitably requires to update the rules that determine the forwarding behaviour of the devices in its data plane. Updating these rules, which are situated on the inherently distributed data plane devices, is an asynchronous process. Hence, packets traversing the network may be processed according to a mixture of new and old rules during the update process. Consequently arising inconsistency effects can severely degrade the network performance and can break stipulated network invariants for instance on connectivity or security. We introduce a general architecture for network management under awareness of expectable update-induced inconsistency effects, which allows for an appropriate selection of an update mechanism and its parameters in order to prevent those effects. We thoroughly analyze update consistency for the case of multicast networks, show crucial particularities and present mechanisms for the prevention and mitigation of multicast-specific inconsistency effects. Observing that on the one hand SDN's separation of control has been deemed rather strict, moving any control ``intelligence'' from the data plane devices to remote controller entities hence increasing control latency while on the other hand the coupling between controller and data plane devices is quite tight hence hindering free distribution of control logic, we present a controller architecture enabling flexible and full-range distribution of network control. The architecture is based on decoupling through an event abstraction and a flexible dissemination scheme for those events based on the content-based publish/subscribe paradigm. This lightweight design allows to push down control logic back onto data plane devices. Thus, we expand SDN's control paradigm and enable the full range from fully decentralized control, over local control still profiting from global view up to fully centralized control. This scheme allows to trade-off scope of state data, consistency semantics and synchronization overhead, control latency, and quality of control decisions. Furthermore, our implementation covers a large set of mechanisms for improving control plane consistency and scalability, such as inherent load-balancing, fast autonomous control decision making, detection of policy conflicts, and a feedback mechanism for data plane updates. In a last area, we focus on the implementation of a distributed application from the domain of message-oriented middleware in the data plane. We implement Complex Event Processing (CEP) on top of programmable network devices employing data plane programming, a recent big trend in SDN, or more specifically, using the P4 language. We discuss challenges entailed in the distributed data plane processing and address aspects of distribution and consistency in particular regarding consistency in stateful data plane programming, where internal state that determines how packets are processed is changed within this very processing, in turn changing the processing of subsequent packets. Since packet processing is executed in parallel on different execution units on the same device sharing the same state data, strong consistency semantics are required in order to ensure application correctness. Enabled by P4's flexible and powerful programming model, our data plane implementation of CEP yields greatly reduced latency and increased throughput. It comprises a compiler that compiles patterns for the detection of complex events specified in our rule specification language to P4 programs, consisting of a state machine and operators that process so-called windows containing historic events.
Open Access
Prosodic event detection for speech understanding using neural networks
(2020) Stehwien, Sabrina; Vu, Ngoc Thang (Prof. Dr.)
Open Access
Reshaping ubiquitous interaction through sensory augmentation
(2020) Kiss, Francisco
Current technological advances enable unprecedented approaches for aiding human perception through digital technologies. Powerful Sensory Augmentation becomes feasible. Our concept aims to improve the natural sensory capabilities of users while transcending the traditional interaction concept of tools. This approach promises to facilitate a more natural interaction with ubiquitous computers and support the enhancement of interaction with reality through computing machines. The goal of this research is to investigate the possibilities of this emerging paradigm and to provide a formal structure for future efforts in this field. In this thesis, we propose a definition for Sensory Augmentation and a design space to structure its study. We present a vision for its application to human activities and identify opportunities and challenges for the incorporation of Sensory Augmentation to human activities. Further, we report on three research probes that help investigate these opportunities and challenges, as well as users’ experiences and preferences for this technology. Each of these probes explores a specific area of augmentation, both assessing specific theoretical aspects of Sensory Augmentation and providing practical insights in the technical challenges posed by the development of applications. Additionally, we investigate and discuss the implications of augmenting the senses from the perspectives of users and society, focusing on benefits but also addressing the main foreseeable negative effects and possible strategies to minimize them. Finally, we present a design process for Sensory Augmentation applications alongside with practical recommendations and discuss future research directions of this new field.
Open Access
Driver alertness monitoring using steering, lane keeping and eye tracking data under real driving conditions
(2020) Friedrichs, Fabian; Yang, Bin (Prof. Dr.-Ing.)
Since humans operate trains, vehicles, aircrafts and industrial machinery, fatigue has always been one of the major causes of accidents. Experts assert that sleepiness is among the major causes of severe road accidents. In-vehicle fatigue detection has been a research topic since the early 80’s. Most approaches are based on driving simulator studies, but do not properly work under real driving conditions. The Mercedes-Benz ATTENTION ASSIST is the ﬁrst highly sophisticated series equipment driver assistance system on the market that detects early signs of fatigue. Seven years of research and development with an unparalleled demand of resources were necessary for its series introduction in 2009 for passenger cars and 2012 for busses. The system analyzes the driving behavior and issues a warning to sleepy drivers. Essentially, this system extracts a single measure (so-called feature), the steering event rate by detecting a characteristic pattern in the steering wheel angle signal. This pattern is principally described by a steering pause followed by a sudden correction. Various challenges had to be tackled for the series-production readiness, such as handling individual driving styles and external inﬂuences from the road, trafﬁc and weather. Fuzzy logic, driving style detection, road condition detection, change of driver detection, ﬁxed-point parameter optimization and sensor surveillance were some of the side results from this thesis that were essential for the system’s maturity. Simply issuing warnings to sleepy drivers is faintly "experiencable" nor transparent. Thus, the next version 2.0 of the system was the introduction of the more vivid ATTENTION LEVEL, which is a permanently available bargraph monitoring the current driving performance. The algorithm is another result of this thesis and was introduced 2013 in the new S-Class. Fatigue is very difﬁcult to grasp since a ground truth reference does not exist. Thus, the presented ﬁndings about camera-based driver monitoring are included as fatigue reference for algorithm training. Concurrently, the presented results build the basis for eye-monitoring cameras of the future generation of such systems. The driver monitoring camera will also play a key role in "automated driving" since it is necessary to know if the driver looks to the road while the vehicle is driving and if he is alert enough to take back control over the vehicle in complex situations. All these improvements represent major steps towards the paradigm of crash free driving. In order to develop and improve the ATTENTION ASSIST, the central goal of the present work was the development of pattern detection and classiﬁcation algorithms to detect fatigue from driving sensors. One major approach to achieve a sufﬁciently high detection rate while maintaining the false alarm rate at a minimum was the incorporation of further patterns with sleepiness-associative ability. Features reported in literature were assessed as well as improved extraction techniques. Various new features were proposed for their applicability under real-road conditions. The mentioned steering pattern detection is the most important feature and was further optimized. Essential series sensor signals, available in most today’s vehicles were considered, such as steering wheel angle, lateral and longitudinal acceleration, yaw rate, wheel rotation rate, acceleration pedal, wheel suspension level, and vehicle operation. Another focus was on the lateral control using camera-based lane data. Under real driving conditions, the effects of sleepiness on the driving performance are very small and severely obscured by external inﬂuences such as road condition, curvature, cross-wind, vehicle speed, trafﬁc, steering parameters etc. Furthermore, drivers also have very different individual driving styles. Short-term distraction from vehicle operation also has a big impact on the driving behavior. Proposals are given on how to incorporate such factors. Since lane features require an optional tracking camera, a proposal is made on how to estimate some lane deviation features from only inertial sensory by means of an extended Kalman ﬁlter. Every feature is related to a number of parameters and implementation details. A highly accelerated method for parameter optimization of the large amount of data is presented and applied to the most promising features. The alpha-spindle rate from the Electroencephalogram (EEG) and Electrooculogram (EOG) were assessed for their performance under real driving conditions. In contrast to the majority of results in literature, EEG was not observed to contribute any useful information to the fatigue reference (except for two drives with microsleeps). Generally, the subjective self-assessments according to the Karolinska Sleepiness Scale and a three level warning acceptance question were consequently used. Various correlation measures and statistical test were used to assess the correlation of features with the reference. This thesis is based on a database with over 27,000 drives that accumulate to over 1.5 mio km of real-road drives. In addition, various supervised real-road driving studies were conducted that involve advanced fatigue levels. The fusion of features is performed by different classiﬁers like Artiﬁcial Neural Networks (ANN) and Support Vector Machines (SVM). Fair classiﬁcation results are achieved with ANN and SVM using cross-validation. A selection of the most potential and independent features is given based on automatic SFFS feature selection. Classical machine learning methods are used in order to yield maximal system transparency and since the algorithms are targeted to run in present control units. The potential of using end-to-end deep learning algorithms is discussed. Whereas its application to CAN-signals is problematic, there is a high potential for driver-camera based approaches. Finally, features were implemented in a real-time demonstrator using an own CAN-interface framework. While various ﬁndings are already rolled out in ATTENTION ASSIST 1.0, 2.0 and ATTENTION LEVEL, it was shown that further improvements are possible by incorporating a selection of steering- and lane-based features and sophisticated classiﬁers. The problem can only be solved on a system level considering all topics discussed in this thesis. After decades of research, it must be recognized that the limitations of indirect methods have been reached. Especially in view of emerging automated driving, direct methods like eye-tracking must be considered and have shown the greatest potential.
Open Access
On the evolvability assurance of microservices : metrics, scenarios, and patterns
(2020) Bogner, Justus; Wagner, Stefan (Prof. Dr.)
Context: Fast moving markets and the age of digitization require that software can be quickly changed or extended with new features. The associated quality attribute is referred to as evolvability: the degree of effectiveness and efficiency with which a system can be adapted or extended. Evolvability is especially important for software with frequently changing requirements, e.g. internet-based systems. Several evolvability-related benefits were arguably gained with the rise of service-oriented computing (SOC) that established itself as one of the most important paradigms for distributed systems over the last decade. The implementation of enterprise-wide software landscapes in the style of service-oriented architecture (SOA) prioritizes loose coupling, encapsulation, interoperability, composition, and reuse. In recent years, microservices quickly gained in popularity as an agile, DevOps-focused, and decentralized service-oriented variant with fine-grained services. A key idea here is that small and loosely coupled services that are independently deployable should be easy to change and to replace. Moreover, one of the postulated microservices characteristics is evolutionary design. Problem Statement: While these properties provide a favorable theoretical basis for evolvable systems, they offer no concrete and universally applicable solutions. As with each architectural style, the implementation of a concrete microservice-based system can be of arbitrary quality. Several studies also report that software professionals trust in the foundational maintainability of service orientation and microservices in particular. A blind belief in these qualities without appropriate evolvability assurance can lead to violations of important principles and therefore negatively impact software evolution. In addition to this, very little scientific research has covered the areas of maintenance, evolution, or technical debt of microservices. Objectives: To address this, the aim of this research is to support developers of microservices with appropriate methods, techniques, and tools to evaluate or improve evolvability and to facilitate sustainable long-term development. In particular, we want to provide recommendations and tool support for metric-based as well as scenario-based evaluation. In the context of service-based evolvability, we furthermore want to analyze the effectiveness of patterns and collect relevant antipatterns. Methods: Using empirical methods, we analyzed the industry state of the practice and the academic state of the art, which helped us to identify existing techniques, challenges, and research gaps. Based on these findings, we then designed new evolvability assurance techniques and used additional empirical studies to demonstrate and evaluate their effectiveness. Applied empirical methods were for example surveys, interviews, (systematic) literature studies, or controlled experiments. Contributions: In addition to our analyses of industry practice and scientific literature, we provide contributions in three different areas. With respect to metric-based evolvability evaluation, we identified a set of structural metrics specifically designed for service orientation and analyzed their value for microservices. Subsequently, we designed tool-supported approaches to automatically gather a subset of these metrics from machine-readable RESTful API descriptions and via a distributed tracing mechanism at runtime. In the area of scenario-based evaluation, we developed a tool-supported lightweight method to analyze the evolvability of a service-based system based on hypothetical evolution scenarios. We evaluated the method with a survey (N=40) as well as hands-on interviews (N=7) and improved it further based on the findings. Lastly with respect to patterns and antipatterns, we collected a large set of service-based patterns and analyzed their applicability for microservices. From this initial catalogue, we synthesized a set of candidate evolvability patterns via the proxy of architectural modifiability tactics. The impact of four of these patterns on evolvability was then empirically tested in a controlled experiment (N=69) and with a metric-based analysis. The results suggest that the additional structural complexity introduced by the patterns as well as developers' pattern knowledge have an influence on their effectiveness. As a last contribution, we created a holistic collection of service-based antipatterns for both SOA and microservices and published it in a collaborative repository. Conclusion: Our contributions provide first foundations for a holistic view on the evolvability assurance of microservices and address several perspectives. Metric- and scenario-based evaluation as well as service-based antipatterns can be used to identify "hot spots" while service-based patterns can remediate them and provide means for systematic evolvability construction. All in all, researchers and practitioners in the field of microservices can use our artifacts to analyze and improve the evolvability of their systems as well as to gain a conceptual understanding of service-based evolvability assurance.

05 Fakultät Informatik, Elektrotechnik und Informationstechnik

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