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Strongly Asymptotically Optimal Methods for the Pathwise Global Approximation of Stochastic Differential Equations with Coefficients of Super-linear Growth
No full textOur subject of study is strong approximation of stochastic differential equations (SDEs) with respect to the supremum and the L_p error criteria, and we seek approximations that are strongly asymptotically optimal in specific classes of approximations. For the supremum error, we prove strong asymptotic optimality for specific tamed Euler schemes relating to certain adaptive and to equidistant time discretizations. For the L_p error, we prove strong asymptotic optimality for specific tamed Milstein schemes relating to certain adaptive and to equidistant time discretizations. To illustrate our findings, we numerically analyze the SDE associated with the Heston–3/2–model originating from mathematical finance
Job Sequencing and Tool Switching Problems with a Generalisation to Non-Identical Parallel Machines
Get PDFManufacturing tools have been dominating the manufacturing process since the 1960s. The job sequencing and tool switching problem is an NP-hard combinatorial optimization that has first been introduced in the context of flexible manufacturing systems in the late 1980s. Since then, production systems have undisputedly changed and improved but manufacturing tools still dominate manufacturing processes. Production and system operation processes are continuously adjusted and optimised to changing customer requirements. If the product variety requires an increasing number of tools for processing that exceeds the local tool magazine capacity of the manufacturing system, tool switches become necessary. Although tool changing times within a manufacturing centre or cell may nowadays be very small due to the high degree of automation, tool switching within a dynamic production environment is still a time consuming process that must be avoided. In order to minimize the total tool setup time to enhance productivity, the objectives of the basic job sequencing and tool switching problem are to sequence a set of jobs and simultaneously to determine the best tool loading. Therefore, job sequencing and tool switching problems are gaining considerable attention.
Several solution approaches to the standard problem and related versions of the problem exist. The first part of this dissertation assesses the current state-of-the-art of the job sequencing and tool switching problem and provides a classification scheme for literature on the job sequencing and tool switching problem and its variations. Only few authors consider generalisations of the problem because the level of complexity of extended problems is high. A general approach of the job sequencing and tool switching problem with non-identical parallel machines and sequence-dependent setup times is described in this dissertation. A novel mathematical model based on time periods is presented and analysed which can be adapted to different objective functions. The last part of this dissertation is a quantitative evaluation of fast and effective construction heuristics as well as of an iterated local search algorithm tested on a new set of benchmark instances. As such this dissertation provides a broad basis for future evaluations of solution approaches to the job sequencing and tool switching problem with non-identical parallel machines and sequence-dependent setup times as well as a basis for further generalisations of the problem like for example tool availability constraints or tool-size dependent variations
New Methods for Improving Embedded Memory Manufacturing Tests
No full textDue to the need for fast and energy-efficient accesses to growing amounts of data, the share and number of embedded memories inside modern microchips has been continuously increasing within the last years. Since embedded memories have the highest integration density of a fabrication technology they pose special test challenges due to complex manufacturing defects as well as strong transistor aging phenomena. This necessitates efficient methods for detecting more subtle defects while keeping test costs low. This work presents novel methods and techniques for improving the efficiency of embedded memory manufacturing tests. The proposed methods are demonstrated in an industrial setting based on production-proven transistor, memory as well as chip models and their benefits over the current state-of-the art is worked out
Feature Interactions in Configurable Software Systems
No full textSoftware has become an important part of our life. Therefore, the number of different applications scenarios and user requirements of software systems grows rapidly. To satisfy these requirements, software vendors build configurable software systems that can be tailored to diverse needs without rebuilding them from scratch, which reduces costs and development time.
Despite considerable advances in software engineering, which allow building high-quality configurable software systems, some challenges remain. One of these challenges is the feature interaction problem that arises when parts (features), from which a configurable system is composed, interact in unexpected ways, and inadvertently change the behavior or quality attributes (such as performance) of the system.
The goal of this dissertation is to systematically study the nature of feature interactions, their causes, their influence on performance of configurable systems, and, based on empirical results, suggest ways of improving techniques for detecting and predicting feature interactions.
More specifically, we compared and evaluated different strategies for the analysis of configurable software systems. The results of our evaluation complement empirical data from previous work about how different analysis strategies for configurable software systems compare with respect to different aspects, such as performance. These results shall be used to develop effective and scalable techniques and tools for analysis of configurable software including feature-interaction detection and prediction techniques and tools.
Technically, we used a machine-learning technique to quantify the influence of feature interactions on performance of real-world configurable systems. We studied the characteristics of interactions that have the largest influence on performance and found that interactions among few features have higher influence than interactions among many features. With a growing number of interacting features, the influence of the corresponding interactions decreases consistently. This implies that interactions involving multiple features can be ignored in practice because of their marginal influence on performance. We also investigated the causes of the interactions and were able to identify several patterns that link these interactions to the architecture of the systems: For example, we found that if a data processing system consisted of multiple features that processed the same data in sequence then these features interacted. The identified patterns can help to anticipate performance interactions already at an early development stage when a system’s architecture is designed.
Furthermore, considering that control-flow interactions (observable at the level of control flow among features) are easier to detect than performance interactions (externally observable through measuring performance of different combinations of features), we conducted a case study on two configurable systems. In this case study, we investigated a possible relation among control-flow feature interactions and performance feature interactions. We also discussed how this relation can be exploited by interaction detection and performance prediction techniques to make them more time efficient and precise. Our case study on two real-world configurable systems revealed that a relation indeed exists, and we were able to show how it can be used to reduce the search space of possibly existing performance interactions. The study can serve as a blueprint for further studies that can rely on our conceptual framework for investigating relations among external and internal interactions.
Overall, the contribution of this dissertation consists of scientific and technical insights, practical tool implementations, empirical evaluations, and case studies that advance the current state of research in the area of feature interactions in configurable software systems. In particular, we provide insights into the causes of feature interactions and their influence on performance of real-world configurable systems (e.g., interaction patterns, decreasing influence of interactions with growing number of involved features). Our results also suggest ways of improving techniques for detecting and predicting feature interactions (e.g., ignoring interactions among multiple features, reducing the search space based on relations among interactions)
Raum und Grenze
No full textRaum und Grenze, und deren Verhältnis zu Individuen wie Kollektiven, stellen dabei zentrale Denkfiguren wie Operatoren der ‚Sinn‘generierung dar. Die folgenden Ausführungen versuchen aus semiotischer Sicht einen Überblick über die verschiedenen Bedeutungen, Artikulationsformen und Anwendungsbereiche dieser Parameter zu geben. Sie orientieren sich an der Skizze, wie sie in Krah 1999 dargelegt wurde, und ergänzen, modifizieren und neujustieren diese aus der Perspektive und dem Stand von 2018
Zirkus und Raum
No full textZirkusperformance ist Raumbewegung. Im Gegensatz zu literarischen oder filmischen Artefakten wird im Zirzensischen daher der Raum zur zentralen, der übrigen Narration hierarchisch übergeordneten Prämisse der narrativen Performanz, ja zur elementaren Ermöglichungsstruktur für die Modellierung eines sekundären semiotischen Systems, das nicht im Rahmen der Semiose nachträglich rekonstruiert, sondern im Moment des Aufführens performativ konstruiert wird. Der vorliegende Artikel fokussiert auf Basis dieser Funktionszusammenhänge des Zirzensischen genau diese Relation von Zirkus und Raum unter der Annahme einer Semiotik der Performanz nach Fischer-Lichte
Context-Aware Worker Selection For Efficient Quality Control In Crowdsourcing
No full textIn the last decade, crowdsourcing has proved its ability to address large scale data collection tasks, such as labeling large data sets, at a low cost and in a short time. However, the performance and behavior variability between workers as well as the variability in task designs and contents, induce an unevenness in the quality of the produced contributions and, thus, in the final output quality. In order to maintain the effectiveness of crowdsourcing, it is crucial to control the quality of the contributions. Furthermore, maintaining the efficiency of crowdsourcing requires the time and cost overhead related to the quality control to be at its lowest. While effective, current quality control techniques such as contribution aggregation, worker selection, context-specific reputation systems, and multi-step workflows, suffer from fairly high time and budget overheads and from their dependency on prior knowledge about individual workers.
In this thesis, we address this challenge by leveraging the similarity between completed and incoming tasks as well as the correlation between the worker declarative profiles and their performance in previous tasks in order to perform an efficient task-aware worker selection. To this end, we propose CAWS (Context AwareWorker Selection) method which operates in two phases; in an offline phase, completed tasks are clustered into homogeneous groups for each of which the correlation with the workers declarative profile is learned. Then, in the online phase, incoming tasks are matched to one of the existing clusters and the correspondent, previously inferred profile model is used to select the most reliable online workers for the given task. Using declarative profiles helps eliminate any probing process, which reduces the time and the budget while maintaining the crowdsourcing quality. Furthermore, the set of completed tasks, when compared to a probing task split, provides a larger corpus from which a more precise profile model can be learned. This translates to a better selection quality, especially for harder tasks.
In order to evaluate CAWS, we introduce CrowdED (Crowdsourcing Evaluation Dataset), a rich dataset to evaluate quality control methods and quality-driven task vectorization and clustering. The generation of CrowdED relies on a constrained sampling approach that allows to produce a task corpus which respects both, the budget and type constraints. Beside helping in evaluating CAWS, and through its generality and richness, CrowdED helps in plugging the benchmarking gap present in the crowdsourcing quality control community.
Using CrowdED, we evaluate the performance of CAWS in terms of the quality of the worker selection and in terms of the achieved time and budget reduction. Results shows the following: first, automatic grouping is able to achieve a learning quality similar to job-based grouping. And second, CAWS is able to outperform the state-of-the-art profile-based worker selection when it comes to quality. This is especially true when strong budget and time constraints are present on the requester side.
Finally, we complement our work by a software contribution consisting of an open source framework called CREX (CReate Enrich eXtend). CREX allows the creation, the extension and the enrichment of crowdsourcing datasets. It provides the tools to vectorize, cluster and sample a task corpus to produce constrained task sets and to automatically generate custom crowdsourcing campaign sites.Im letzten Jahrzehnt hat Crowdsourcing seine Fähigkeit bewiesen große Datensammelaufgaben, wie die Beschriftung großer Datensätze, zu geringen Kosten und in kurzer Zeit zu bewältigen. Die Leistungs- und Verhaltensschwankungen zwischen den Arbeitern sowie die Variabilität in den Aufgabenentwürfen und -inhalten führen jedoch zu einer Ungleichmäßigkeit in der Qualität der erworbenen Beiträge und somit in der endgültigen Ausgabequalität.
Um die Effektivität von Crowdsourcing zu erhalten, ist es entscheidend die Qualität der einzelnen Beiträge zu kontrollieren. Darüber hinaus erfordert die Aufrechterhaltung der Effizienz von Crowdsourcing, dass der Zeit- und Kostenaufwand für die Qualitätskontrolle am geringsten ist. Effektive, aktuelle Qualitätskontrolltechniken wie die Aggregation von Beiträgen, die gezielte Auswahl von Arbeitern, kontextspezifische Reputationssysteme und mehrstufige Workflows leiden unter ziemlich hohen Zeit- und Budgetzwangslagen und von ihrer Abhängigkeit von vorausgehenden Kenntnissen über die einzelnen Arbeiter.
Ìn dieser Arbeit gehen wir diese Herausforderungen an, indem wir die Ähnlichkeit zwischen abgeschlossenen und eingehenden Aufgaben sowie die Korrelation zwischen den von Arbeitern deklarierten Profilen und deren Leistung in früheren Aufgaben nutzen, um eine effiziente aufgabenbewusste Arbeiterauswahl durchzuführen. Zu diesem Zweck schlagen wir eine zweiphasige Methode vor: CAWS (Context Aware Worker Selection). In einer Offline-Phase werden bereits bearbeitete Aufgaben in homogene Cluster gruppiert, für welche jeweils die Korrelation mit dem vorab deklarierten Profil der Arbeiter erlernt wird.
In der Online-Phase werden eingehende Aufgaben dann einem der vorhandenen Cluster zugeordnet, und das entsprechende, zuvor erschlossene Profilmodell wird dazu verwendet, um die vertrauenswürdigsten Online-Mitarbeiter für die gegebene Aufgabe auszuwählen.
Die Verwendung von deklarativen Profilen hilft dabei jeglichen Sondierungsprozess zu eliminieren, wobei Zeit und Kosten reduziert werden und gleichzeitig die Crowdsourcing-Qualität beibehalten wird. Darüber hinaus bietet das Aggregat der abgeschlossenen Aufgaben im Vergleich zu einer Aufgabenaufteilung durch Sondierung einen größeren Korpus, aus dem ein präziseres Profilmodell erlernt werden kann. Dies führt zu einer besseren Auswahlqualität, insbesondere für schwierigere Aufgaben.
Um CAWS zu evaluieren, stellen wir CrowdED (Crowdsourcing Evaluation Dataset) vor, einen umfassenden Datensatz zur Evaluierung von Qualitätskontrollmethoden und qualitätsgetriebener Aufgaben-Vektorisierung und Clusterbildung. Die Generierung von CrowdED basiert auf einem bedingten Stichprobeverfahren, welches es ermöglicht, einen Aufgaben-Corpus zu erstellen, der sowohl die Budget- als auch die Typ-Bedingungen einhält.
Neben seiner Allgemeingültigkeit und Reichhaltigkeit, hilft CrowdED nicht nur bei der Bewertung von CAWS, sondern es hilft auch dabei, die Benchmarking-Lücke in der Crowdsourcing-Community für Qualitätskontrolle zu schließen.
Mit CrowdED evaluieren wir die Leistung von CAWS im Hinblick auf die Qualität der Arbeiterauswahl und auf die erreichte Zeit- und Kostenreduzierung. Die Ergebnisse zeigen folgendes: Zum einen kann mit der automatischen Gruppierung eine Lernqualität ähnlich der von Job-basierten Gruppierungen erreicht werden. Und zweitens ist CAWS in der Lage, die aktuellen profilbasierten Auswahlmethoden in Bezug auf Qualität zu übertreffen. Dies gilt insbesondere dann, wenn auf der Anfordererseite starke Budget- und Zeitbeschränkungen bestehen.
Schließlich ergänzen wir unsere Arbeit mit einer Software, die aus einem lizenzfreien Framework namens CREX (CReate Enrich eXtend) besteht. CREX ermöglicht die Erstellung, Erweiterung und Anreicherung von Crowdsourcing-Datensätzen. Es liefert die nötigen Werkzeuge um einen Aufgabenkorpus zu vektorisieren, zu gruppieren und zu samplen, um eingeschränkte Aufgabensätze zu erzeugen und um automatisch benutzerdefinierte Crowdsourcing-Kampagnen-Seiten zu generieren
Vibrancy of Public Spaces: Inclusivity and Participation Amidst the Challenges in Transformative Process in the City of Cagayan de Oro, Philippines
Get PDFThis study examines the dynamics which lead to revitalization of everyday life in the public spaces of Cagayan de Oro, a medium-sized urban center in Northern Mindanao, the Philippines. By employing the oriental philosophies together with western thoughts such as Henri Lefebvre, Alain Touraine and Jürgen Habermas, this study elucidates that the core of perceived, lived and conceived spaces is ‘the Subject.’ Once the Subject utilizes the public sphere to instill social action, social space is ultimately produced. Hawkers, grassroots environmental activists, street readers and artists are the social Subjects who partake in the vibrancy of public spaces. The social Subjects utilize public spaces as venues of social transformation. Thus, this study argues that the social Subjects’ role in democratic process lead to inclusivity of the marginalized sector in the public spaces of the city
Binnentourismus in den ASEAN-Staaten - unter besonderer Berücksichtigung Thailands
No full textDer Binnentourismus in den ASEAN-Staaten wird an Hand von vorhandenen statistischen Daten untersucht. Durch Befragungen thailändischer Binnentouristen wird darüber hinaus die Datenerhebung ausgeweitet, um die vorhandenen statistischen Daten zu ergänzen und zu verifizieren. Hierbei wird insbesonders untersucht, in wieweit der Binnentourismus in den Ländern wahrgenommen wird, welche Bevölkerungsgruppen sich daran beteiligen, welche Interessen sie dabei verfolgen und welche Auswirkungen der Binnentourismus in physiognomischer und sozio-kultureller Hinsicht mit sich bringt
Information in Online Purchase Decision Processes: Information Extraction, Data Analysis and Economic Impact
No full textInformation has a particular importance in online purchase decision processes. As opposed to consumers in online markets, consumers in online markets cannot inspect the physical product to evaluate it and reduce their perceived risk. Hence, consumers in online markets are dependent upon the information that they can gather about a product in which they are interested. Therefore, they have two primary sources of information: Product descriptions and customer reviews. Both sources affect consumers’ purchase decision processes and hence have an economic impact for consumers, shop providers and manufacturers. It is important to know how these sources of information influence a customer’s online purchase decision and how to extract the relevant information.
To examine these research objectives, several studies applying different methodological approaches have been conducted. The results are presented in this dissertation