In many real-world applications today, it is critical to continuously record and monitor certain machine or system health indicators to discover malfunctions or other abnormal behavior at an early... Show moreIn many real-world applications today, it is critical to continuously record and monitor certain machine or system health indicators to discover malfunctions or other abnormal behavior at an early stage and prevent potential harm. The demand for such reliable monitoring systems is expected to increase in the coming years. Particularly in the industrial context, in the course of ongoing digitization, it is becoming increasingly important to analyze growing volumes of data in an automated manner using state-of-the-art algorithms. In many practical applications, one has to deal with temporal data in the form of data streams or time series. The problem of detecting unusual (or anomalous) behavior in time series is commonly referred to as time series anomaly detection. Anomalies are events observed in the data that do not conform to the normal or expected behavior when viewed in their temporal context.This thesis focuses on unsupervised machine learning algorithms for anomaly detection in time series. In an unsupervised learning setup, a model attempts to learn the normal behavior in a time series — which might already be contaminated with anomalies — without any external assistance. The model can then use its learned notion of normality to detect anomalous events. Show less
The research topic of the thesis is the extension of evolutionary multi-objective optimization for real-world scheduling problems. Several novel algorithms are proposed: the diversity indicator... Show moreThe research topic of the thesis is the extension of evolutionary multi-objective optimization for real-world scheduling problems. Several novel algorithms are proposed: the diversity indicator-based multi-objective evolutionary algorithm (DI-MOEA) can achieve a uniformly distributed solution set; the preference-based MOEA can obtain preferred solutions; the edge-rotated cone can improve the performance of MOEAs for many-objective optimization; and dynamic MOEA takes the stability as an extra objective. Besides the classical flexible job shop scheduling, the thesis proposes solutions for the novel problem domain of vehicle fleet maintenance scheduling optimization (VFMSO). The problem originated from the CIMPLO (Cross-Industry Predictive Maintenance Optimization Platform) project and the project partners Honda and KLM. The VFMSO problem is to determine the maintenance schedule for the vehicle fleet, meaning to find the best maintenance order, location and time for each component in the vehicle fleet based on the predicted remaining useful lifetimes of components and conditions of available workshops. The maintenance schedule is optimized to bring business advantages to industries, i.e., to reduce maintenance time, increase safety and save repair expenses. After formulating the problem as a scalable benchmark in an industrially relevant setting, the proposed algorithms have been successfully used to solve VFMSO problem instances. Show less
In this thesis it is posed that the central object of preference discovery is a co-creative process in which the Other can be represented by a machine. It explores efficient methods to enhance... Show moreIn this thesis it is posed that the central object of preference discovery is a co-creative process in which the Other can be represented by a machine. It explores efficient methods to enhance introverted intuition using extraverted intuition's communication lines. Possible implementations of such processes are presented using novel algorithms that perform divergent search to feed the users' intuition with many examples of high quality solutions, allowing them to take influence interactively. The machine feeds and reflects upon human intuition, combining both what is possible and preferred. The machine model and the divergent optimization algorithms are the motor behind this co-creative process, in which machine and users co-create and interactively choose branches of an ad hoc hierarchical decomposition of the solution space.The proposed co-creative process consists of several elements: a formal model for interactive co-creative processes, evolutionary divergent search, diversity and similarity, data-driven methods to discover diversity, limitations of artificial creative agents, matters of efficiency in behavioral and morphological modeling, visualization, a connection to prototype theory, and methods to allow users to influence artificial creative agents. This thesis helps putting the human back into the design loop in generative AI and optimization. Show less
In today's volatile market environments, companies must be able to continuously innovate. In this context, innovation does not only refer to the development of new products or business models but... Show moreIn today's volatile market environments, companies must be able to continuously innovate. In this context, innovation does not only refer to the development of new products or business models but often also affects the entire organization, which has to transform its structures, processes, and ways of working.Corporate entrepreneurship (CE) programs are often used by established companies to address these innovation and transformation challenges. In general, they are understood as formalized entrepreneurial activities to (1) support internal corporate ventures or (2) work with external startups. The organizational design and value creation of CE programs exhibit a high degree of heterogeneity. On the one hand, this heterogeneity makes CE programs a valuable management tool that can be used for many purposes. On the other hand, it can be seen as a reason for the current challenges that companies experience in effectively using and managing CE programs.By systematically analyzing 54 different cases in established companies in Germany, Switzerland, and Austria, this study contributes to a better understanding of the heterogeneity of CE programs. The taxonomic approach provides clearly defined types of CE programs that are distinguished according to their organizational design and the outputs they generate. Show less
In this work, we attempt to answer the question: "How to learn robust and interpretable rule-based models from data for machine learning and data mining, and define their optimality?".Rules provide... Show moreIn this work, we attempt to answer the question: "How to learn robust and interpretable rule-based models from data for machine learning and data mining, and define their optimality?".Rules provide a simple form of storing and sharing information about the world. As humans, we use rules every day, such as the physician that diagnoses someone with flu, represented by "if a person has either a fever or sore throat (among others), then she has the flu.". Even though an individual rule can only describe simple events, several aggregated rules can represent more complex scenarios, such as the complete set of diagnostic rules employed by a physician.The use of rules spans many fields in computer science, and in this dissertation, we focus on rule-based models for machine learning and data mining. Machine learning focuses on learning the model that best predicts future (previously unseen) events from historical data. Data mining aims to find interesting patterns in the available data.To answer our question, we use the Minimum Description Length (MDL) principle, which allows us to define the statistical optimality of rule-based models. Furthermore, we empirically show that this formulation is highly competitive for real-world problems. Show less
This thesis mainly focuses on cross-modal retrieval and single-modal image retrieval via deep learning methods, i.e. by using deep convolutional neural networks.For cross-modal retrieval, Shannon... Show moreThis thesis mainly focuses on cross-modal retrieval and single-modal image retrieval via deep learning methods, i.e. by using deep convolutional neural networks.For cross-modal retrieval, Shannon information entropy and adversarial learning are integrated to learn a common latent space for image data and text data. Furthermore, this thesis explores single-modal image retrieval in an incremental learning context to reduce the catastrophic forgetting of deep models, thereby expanding the continuous retrieval ability. The efficacy of the proposed methods in this thesis is verified by thorough experiments on the considered datasets. This thesis also gives an overview of new ideas and trends for multimodal content understanding. Show less
Interactive exploration of large volumes of data is increasingly common, as data scientists attempt to extract interesting information from large opaque data sets. This scenario presents a... Show moreInteractive exploration of large volumes of data is increasingly common, as data scientists attempt to extract interesting information from large opaque data sets. This scenario presents a difficult challenge for traditional database systems, as (1) nothing is known about the query workload in advance, (2) the query workload is constantly changing, and (3) the system must provide interactive responses to the issued queries. This environment is challenging for index creation, as traditional database indexes require upfront creation, hence a priori workload knowledge, to be efficient.In this work, we introduce Progressive Indexing, a novel performance-driven indexing technique that focuses on automatic index creation while providing interactive response times to incoming queries. Its design allows queries to have a limited budget to spend on index creation. The indexing budget is automatically tuned to each query before query processing. This allows for systems to provide interactive answers to queries during index creation while being robust against various workload patterns and data distributions.We develop progressive algorithms to index one and multiple dimensions. In addition, we introduce Progressive Merges, a robust algorithm that merges appends into our Progressive Indexes without penalizing single queries. Show less
In deep reinforcement learning, searching and learning techniques are two important components. They can be used independently and in combination to deal with different problems in AI. These... Show moreIn deep reinforcement learning, searching and learning techniques are two important components. They can be used independently and in combination to deal with different problems in AI. These results have inspired research into artificial general intelligence (AGI).We study table based classic Q-learning on the General Game Playing (GGP) system, showing that classic Q-learning works on GGP, although convergence is slow, and it is computationally expensive to learn complex games.This dissertation uses an AlphaZero-like self-play framework to explore AGI on small games. By tuning different hyper-parameters, the role, effects and contributions of searching and learning are studied. A further experiment shows that search techniques can contribute as experts to generate better training examples to speed up the start phase of training.In order to extend the AlphaZero-likeself-play approach to single player complex games, the Morpion Solitaire game is implemented by combining Ranked Reward method. Our first AlphaZero-based approach is able to achieve a near human best record.Overall, in this thesis, both searching and learning techniques are studied (by themselves and in combination) in GGP and AlphaZero-like self-play systems. We do so for the purpose of making steps towards artificial general intelligence, towards systems that exhibit intelligent behavior in more than one domain. Show less
Natural history collections provide invaluable sources for researchers with different disciplinary backgrounds, aspiring to study the geographical distribution of flora and fauna across the globe... Show moreNatural history collections provide invaluable sources for researchers with different disciplinary backgrounds, aspiring to study the geographical distribution of flora and fauna across the globe as well as other evolutionary processes. They are of paramount importance for mapping out long-term changes: from culture, to ecology, to how natural history is practiced.This thesis describes computational methods for knowledge extraction from archives of natural history collections---here referring to handwritten manuscripts and hand-drawn illustrations. As we are dealing with heterogeneous real-world data, the task becomes exceptionally challenging. Small samples and a long-tailed distribution, sometimes with very fine-grained distinctions between classes, hamper model learning. Prior knowledge is therefore needed to bootstrap the learning process. Moreover, archival content can be difficult to interpret and integrate, and should therefore be formally described for data integration within and across collections. By serving extracted knowledge to the Semantic Web, collections are made amenable for research and integration with other biodiversity resources on the Web. Show less
Today, knowledge is the most crucial element to stimulate organizational competitiveness and economic development. The ability of a firm to quickly recognize, assimilate, and utilize external... Show moreToday, knowledge is the most crucial element to stimulate organizational competitiveness and economic development. The ability of a firm to quickly recognize, assimilate, and utilize external knowledge is one of the core capabilities that bring organizational competitive advantages. Such an ability is called absorptive capacity (AC). This study focuses on three AC-related topics in the context of Chinese SMEs: 1) How do SMEs absorb external knowledge in terms of its recognition, assimilation, and utilization? 2) What challenges do SMEs face when absorbing external knowledge? And, 3)Which knowledge assimilation mechanisms do have an impact on the performance of SMEs? Show less
A New Technology-Based Firm (NTBF) is a significant enabler of job creation and a driver of the economy through stimulating innovation. In the last two decades, we have seen an enormous development... Show moreA New Technology-Based Firm (NTBF) is a significant enabler of job creation and a driver of the economy through stimulating innovation. In the last two decades, we have seen an enormous development of the NTBFs. However, the liability of smallness, newness, and weak networking ties are three important obstacles in the early stages of an NTBF’s lifecycle. Consequently, there is a high rate of failure among NTBFs.A remedy to avoid these failures is in using the support and resources by Business Incubators (BIs). BIs provide supportive services to promote the NTBFs capabilities and to help them address their liabilities.So far, there is almost no reliable evidence on the effectiveness of BIs on the performance of NTBFs. Therefore, we aim to identify the supportive activities by BIs and, to understand to what extent the supports by them have a serious impact on the performance of their NTBFs. Building on qualitative and quantitative research methods, a model to measure the impact of support by BIs on the performances of NTBFs is developed, and tested among Dutch and German NTBFs. The research results provide practical guidelines for the management teams of the incubators, which can increase the effectiveness of their performances. Show less
Miniaturized satellites enable a variety space missions which were in the past infeasible, impractical or uneconomical with traditionally-designed heavier spacecraft. Especially CubeSats can be... Show moreMiniaturized satellites enable a variety space missions which were in the past infeasible, impractical or uneconomical with traditionally-designed heavier spacecraft. Especially CubeSats can be launched and manufactured rapidly at low cost from commercial components, even in academic environments. However, due to their low reliability and brief lifetime, they are usually not considered suitable for life- and safety-critical services, complex multi-phased solar-system-exploration missions, and missions with a longer duration. Commercial electronics are key to satellite miniaturization, but also responsible for their low reliability: Until 2019, there existed no reliable or fault-tolerant computer architectures suitable for very small satellites. To overcome this deficit, a novel on-board-computer architecture is described in this thesis.Robustness is assured without resorting to radiation hardening, but through software measures implemented within a robust-by-design multiprocessor-system-on-chip. This fault-tolerant architecture is component-wise simple and can dynamically adapt to changing performance requirements throughout a mission. It can support graceful aging by exploiting FPGA-reconfiguration and mixed-criticality. Experimentally, we achieve 1.94W power consumption at 300Mhz with a Xilinx Kintex Ultrascale+ proof-of-concept, which is well within the powerbudget range of current 2U CubeSats. To our knowledge, this is the first COTS-based, reproducible on-board-computer architecture that can offer strong fault coverage even for small CubeSats. Show less
Multi-objective evolutionary computation aims to find high quality (Pareto optimal) solutions that represent the trade-off between multiple objectives. Within this field there are a number of key... Show moreMulti-objective evolutionary computation aims to find high quality (Pareto optimal) solutions that represent the trade-off between multiple objectives. Within this field there are a number of key challenges. Among others, this includes constraint handling and the exploration of mixed-integer search spaces. This thesis investigates how these challenges can be handled at the same time, and in particular how they can be applied in the multi-objective optimisation algorithms. These algorithms are developed in the context of the optimisation of building spatial designs, which describe the exterior shape of a building, and the internal division into different spaces. Spatial designs are developed early in the design process, and thus have a large impact on the final building design, and in turn also on the quality of the building. Here the structural and thermal performance of a building are optimised to reduce resource consumption. The main contributions of this thesis are as follows. Firstly, a representation for building spatial designs in is introduced. Secondly, specialised search operators are designed to ensure only feasible solutions will be explored. Thirdly, data about the discovered solutions is analysed to explain the results to domain experts. Finally, a general purpose multi-objective mixed-integer evolutionary algorithm is developed. Show less
Chip manufacturers are rapidly moving towards so-called manycore chips with thousands of independent processors on the same silicon real estate. Current programming languages can only leverage the... Show moreChip manufacturers are rapidly moving towards so-called manycore chips with thousands of independent processors on the same silicon real estate. Current programming languages can only leverage the potential power by inserting code with low level concurrency constructs, sacrificing clarity. Alternatively, a programming language can integrate a thread of execution with a stable notion of identity, e.g., in active objects.Abstract Behavioural Specification (ABS) is a language for designing executable models of parallel and distributed object-oriented systems based on active objects, and is defined in terms of a formal operational semantics which enables a variety of static and dynamic analysis techniques for the ABS models.The overall goal of this thesis is to extend the asynchronous programming model and the corresponding analysis techniques in ABS. Show less
Over the last decades, aviation safety has improved strongly. As a downside, airline pilots do not have as many opportunities to develop through experience the competencies that they need in... Show moreOver the last decades, aviation safety has improved strongly. As a downside, airline pilots do not have as many opportunities to develop through experience the competencies that they need in critical situations. However, competencies can also be developed through training. In aviation, this is being done through simulator training. The thesis explores whether competencies can be developed through training with the use of serious games. Assuming that the competencies needed to act adequately in critical situations can be trained in games, the relatively scarce flight simulators can then be fully dedicated to the training of technical skills.We investigate whether games are a suitable training method to develop competencies, and we examine whether airline pilots will accept to be trained through games. Furthermore, the thesis investigates the effect of voluntary gameplay on the outcomes of a serious game. The outcomes of our studies show that that mandatorily playing a serious game does not ruin the fun, games can be designed in such a way that they support competency development, and that pilots are open to using serious games for training. As part of pilot training, game-based learning has potential, but designing an effective game should not be underestimated. Show less
Mining time series is a machine learning subfield that focuses on a particular data structure, where variables are measured over (short or long) periods of time. In this thesis we focus on... Show moreMining time series is a machine learning subfield that focuses on a particular data structure, where variables are measured over (short or long) periods of time. In this thesis we focus on multivariate time series, with multiple vari- ables measured over the same period of time. In most cases, such variables are collected at different sampling rates. When combined, these variables can be explored with machine learning methods for multiple purposes.Firstly, we consider the possibility of unsupervised learning. In this case, we propose a pattern recognition method that discovers subsets of variables that show consistent behavior in a number of shared time segments. Fur- thermore, when in a supervised setting, given a dependent variable (target),we propose a method that aggregates independent variables into meaningful features.Additionally to the methods above, we provide two tools in the form of Software as a Service, where users without programming background can intuitively follow the learning and testing methodologies for both methods.Finally, we present an applied study of machine learning to improve speed skating athletes performance. Here, we make a deep analysis of historical data, in order to help optimize performance results. Show less
This thesis seeks to combine two different research topics; Multi-Objective Optimization and Complex Network Analysis. Multi-Objective Optimization aims at finding a set of optimal, non-dominated... Show moreThis thesis seeks to combine two different research topics; Multi-Objective Optimization and Complex Network Analysis. Multi-Objective Optimization aims at finding a set of optimal, non-dominated solutions for optimization problems with multiple (actually, many) conflicting objectives. There is a wide range of applications of multi-objective optimization such as in science, engineering design, network analysis, chemical processes, delivery of products, economics and logistics, medical health and so forth. Since one often faces problems with a larger number of objective functions to be optimized simultaneously, the research topic has shifted to Many-Objective Optimization, which means optimization with (far) more than three objective functions. Complex network analysis is a research field that deals with analyzing large networks. In this research line, there are some active research topics, such as controlling complex networks, finding communities in a network, and measuring the importance of nodes in networks. Due to the bigger amount of data and more difficult problems arising in complex network analysis, research in this field has increased significantly. To this end, more complex networks has given the challenge of finding better approaches in dealing with the problem to yield some adequate result of an analysis. Show less
This thesis is about augmented reality (AR). AR is commonly considered a technology that integrates virtual images into a user’s view of the real world. Yet, this thesis is not about such... Show moreThis thesis is about augmented reality (AR). AR is commonly considered a technology that integrates virtual images into a user’s view of the real world. Yet, this thesis is not about such technologies. We believe a technology-based notion of AR is incomplete. In this thesis, we challenge the technology-oriented view, provide new perspectives on AR and propose a different understanding. We argue that AR is characterized by the relationships between the virtual and the real and approach AR from a fundamental, experience-focused view. By doing so, we create an unusually broad and diverse image of what AR is, or arguably could be. We discuss the fundamental characteristics of AR and the many possible manifestations it can take and propose new, imaginative AR environments that have no counterpart in a purely physical world. Show less
Including modelling as part of software development appears to have various benefits. Why then is it that not all companies use software modelling? One of the main reasons is that it requires up... Show moreIncluding modelling as part of software development appears to have various benefits. Why then is it that not all companies use software modelling? One of the main reasons is that it requires up-front investments. From an economic point of view, any type of investment must be justified in terms of how much payback there will be at a later stage. This being the case, in the context of software projects, investment in modelling should be justified by benefits, such as improved productivity and improved product quality, which can be seen later during software development or maintenance. When such benefits are not tangible or foreseeable, modelling becomes a practice without clear added value for the system being developed. The problem, therefore, is how we can investigate and prove whether or not modelling, or some specific characteristics of modelling, provide any benefits during software development and maintenance. As long as this question remains unanswered, it will be difficult to motivate and justify modelling activities in real software projects. This thesis therefore contributes to partially answering these open questions by focusing the empirical research on the benefits of using UML modelling during software maintenance. Show less
Continuous optimization is never easy: the exact solution is always a luxury demand and the theory of it is not always analytical and elegant. Continuous optimization, in practice, is... Show moreContinuous optimization is never easy: the exact solution is always a luxury demand and the theory of it is not always analytical and elegant. Continuous optimization, in practice, is essentially about the efficiency: how to obtain the solution with same quality using as minimal resources (e.g., CPU time or memory usage) as possible? In this thesis, the number of function evaluations is considered as the most important resource to save. To achieve this goal, various efforts have been implemented and applied successfully. One research stream focuses on the so-called stochastic variation (mutation) operator, which conducts an (local) exploration of the search space. The efficiency of those operator has been investigated closely, which shows a good stochastic variation should be able to generate a good coverage of the local neighbourhood around the current search solution. This thesis contributes on this issue by formulating a novel stochastic variation that yields good space coverage. Show less
