05 Fakultät Informatik, Elektrotechnik und Informationstechnik
Permanent URI for this collectionhttps://elib.uni-stuttgart.de/handle/11682/6
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Item 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.Item 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.Item 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.Item Open Access Reshaping ubiquitous interaction through sensory augmentation(2020) Kiss, FranciscoCurrent 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.Item Open Access Prosodic event detection for speech understanding using neural networks(2020) Stehwien, Sabrina; Vu, Ngoc Thang (Prof. Dr.)Item 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.Item Open Access Der Germanium-Zener-Emitter für die Silizium-Photonik(2020) Körner, Roman; Schulze, Jörg (Prof. Dr. habil.)Item 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.Item Open Access Utilizing networked mobile devices for scientific simulations(2020) Dibak, Christoph; Rothermel, Kurt (Prof. Dr.)Numerical simulations on mobile devices create new applications supporting engineers and scientists in the field. Boosted by novel augmented reality devices, in-field analysis of complex systems allow engineers to make better decisions and predict the behavior of such systems by assuming different parameters before making risky and costly decisions. Mobile simulations are challenging as battery-powered mobile devices are only equipped with slow processors and are limited in energy resources. At the same time, mobile devices are only connected via wireless communication subjected to environmental conditions that might cause slow bandwidths or even disconnections to remote computing resources. Nevertheless, concepts presented in this thesis assume a distributed computation between mobile device and a powerful remote server. This thesis covers three major areas of the research field of mobile simulations. First, it provides concepts for distributed execution between server and mobile device in case of frequent disconnections. Second, it provides concepts using computationally less complex surrogate models for faster computation on the mobile device while still utilizing remote resources. Third, it provides concepts utilizing model order reduction for fast execution on mobile devices by pre-computing and adaptation of reduced models on a connected server. Evaluations show that concepts presented in this thesis significantly increase the performance of mobile simulations. In the case of disconnections, the number of deadline misses is reduced by 61 % while reducing the energy consumption by more than 74 % compared to a simplified approach. Concepts utilizing surrogate models speed-up the computation of the simulation by a factor of 6.5. Lastly, concepts utilizing model order reduction reduce the time for the computation of simulation results by a factor of 131 while using 73 times less energy for the specific test application.Item Open Access A model-based approach for data processing in IoT environments(2020) Franco da Silva, Ana Cristina; Mitschang, Bernhard (Prof. Dr.-Ing. habil.)The recent advances in several areas, including sensor technologies, networking, and data processing, have enabled the Internet of Things (IoT) vision to become more and more a reality every day. As a consequence of these advances, the IoT of today allows the development of sophisticated applications for IoT environments, such as smart cities, smart homes, or smart factories. Due to continuous sensor measurements and frequent data exchange among so-called IoT objects, the data generated within an IoT environment incorporate the form of data streams. With this increasing amount of data to be continuously processed, several challenges arise while aiming at an efficient processing of IoT data. For instance, how IoT data processing can be realized, so that meaningful information can be derived without affecting the reactiveness of IoT applications. Furthermore, how different functional, non-functional, and user-defined requirements of IoT applications can be satisfied by the IoT data processing. In this PhD thesis, a new holistic approach for processing data stream-based applications within IoT environments is presented. Its focus lies on efficient placement of operators of data stream applications onto heterogeneous, distributed, dynamic IoT environments. In contrast to state-of-the-art operator placement, this approach takes into consideration additional requirements introduced by the peculiar characteristics of the Internet of Things. Furthermore, non-functional and user-defined requirements are also taken into consideration. This PhD thesis is supported by different informational models and operator placement techniques, so that the entire life cycle of IoT environments and data stream-based applications can be easily managed. IoT environments and their processing capabilities are described by IoT environment models (IoTEM). Likewise, the business logic of IoT applications and their requirements are defined by data stream processing models (DSPM). Based on these informational models, several algorithms determine feasible placements of processing operators onto IoT objects of IoT environments, so that the aforementioned requirements and capabilities are matched. In this approach, one of the main goals is to process IoT data as near to data sources as possible, so that cloud infrastructures are employed only in cases where IoT environments do not offer sufficient processing resources for the IoT application. The execution of data processing on both IoT environments and cloud infrastructures is commonly known as fog computing. Through the approach of this PhD thesis, data processing of IoT applications can be tailored to particular use cases, supporting the specific requirements of the domains, and furthermore, of IoT application users. Once feasible placements are determined, processing operators are then deployed onto corresponding IoT objects using standards, such as TOSCA, and the IoT application is considered up and running. Finally, the IoT environment is continuously monitored in order to recognize and react to disturbances affecting the data processing of deployed IoT applications. The approach of this PhD thesis is supported by the Multi-purpose Binding and Provisioning Platform (MBP), an open-source IoT platform, which has been developed as a proof-of-concept of the contributions of this PhD thesis.
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