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In the engineering domain, representing real-world objects using a body of data, called a digital twin, which is frequently updated by “live” measurements, has shown various advantages over tradi- tional modelling and simulation techniques. Consequently, urban planners have a strong interest in digital twin technology, since it provides them with a laboratory for experimenting with data before making far-reaching decisions. Realizing these decisions involves the work of professionals in the architecture, engineering and construction (AEC) domain who nowadays collaborate via the methodology of building information modeling (BIM). At the same time, the citizen plays an integral role both in the data acquisition phase, while also being a beneficiary of the improved resource management strategies. In this paper, we present a prototype for a “digital energy twin” platform we designed in cooperation with the city of Regensburg. We show how our extensible platform de- sign can satisfy the various requirements of multiple user groups through a series of data processing solutions and visualizations, in- dicating valuable design and implementation guidelines for future projects. In particular, we focus on two example use cases concern- ing building electricity monitoring and BIM. By implementing a flexible data processing architecture we can involve citizens in the data acquisition process, meeting the demands of modern users regarding maximum transparency in the handling of their data.
In the realm of parallel computing, optimization plays a pivotal role in achieving efficient and scalable solutions. In this work, we present the parallelization of a hybrid genetic search for solving the Capacitated Vehicle Routing Problem with Pickup and Delivery (CVRPPD).It leverages the synergy between genetic algorithms and parallel computing to address the complex optimization problem. This hybrid algorithm combines a customized version of local search with a genetic algorithm to compute an effective solution. Our implementation makes use of the Message Passing Interface (MPI) for data distribution and parallel execution. In addition, we run multi-threaded processes on NVIDIA graphical processors using the CUDA technology, which further increases the computation speed and consequently minimizes the runtime. Parallelization also allows the best-improvement strategy to be used instead of the rst-improvement strategy while maintaining the same runtime. We store the resulting routes in a bus route database which we created as the basis of an extensive library of optimal routes for our specifc use case of optimizing bus routes in a rural area. The experimental results on real road data show that the parallel implementation of the Hybrid Genetic Search (HGS) achieves significant improvements in runtime over the sequential implementation above a certain problem size. We believe that our implementation of the parallel hybrid genetic search method can have a great in influence on optimization strategies in parallel computing and can also be applied to other subproblems of the VRP.
Moving Object Databases are designed to store and process database objects with attributes that can change over time. Simple examples are moving points, that change position over time, a bit more complex are moving regions, that can also change shape. The spatial and spatiotemporal object types in current moving objects databases are limited to two dimensions. This work strives to extend the set of spatial moving object types into the third and even higher dimensions while preserving a consistent family of operations for it. A robust algorithm for the interpolation of two regions to a moving region of any dimensionality is developed, as well as the fundamental ideas for several other operations.
Finding the optimal join order (JO) is one of the most important problems in query optimisation, and has been extensively considered in research and practise. As it involves huge search spaces, approximation approaches and heuristics are commonly used, which explore a reduced solution space at the cost of solution quality. To explore even large JO search spaces, we may consider special-purpose software, such as mixed-integer linear programming (MILP) solvers, which have successfully solved JO problems. However, even mature solvers cannot overcome the limitations of conventional hardware prompted by the end of Moore’s law. We consider quantum-inspired digital annealing hardware, which takes inspiration from quantum processing units (QPUs). Unlike QPUs, which likely remain limited in size and reliability in the near and mid-term future, the digital annealer (DA) can solve large instances of mathematically encoded optimisation problems today. We derive a novel, native encoding for the JO problem tailored to this class of machines that substantially improves over known MILP and quantum-based encodings, and reduces encoding size over the state-of-the-art. By augmenting the computation with a novel readout method, we derive valid join orders for each solution obtained by the (probabilistically operating) DA. Most importantly and despite an extremely large solution space, our approach scales to practically relevant dimensions of around 50 relations and improves result quality over conventionally employed approaches, adding a novel alternative to solving the long-standing JO problem.
This paper examines the conceptualization of sustainability in the context of information and communication technology (ICT) research. Through an inductive text analysis of sixteen literature reviews spanning from 2014 to 2023, key themes and concepts are identified, highlighting the complex relationship between ICT and sustainability. ICT is perceived both as an enabler and a problem for sustainability. Furthermore, the terminology and concept of sustainability in the context of ICT remain unclear. The emergence of digitalization as a novel socio-technical phenomenon poses additional challenges for conceptual alignment. While a holistic view of sustainability in ICT is desired, business and social implications receive less attention. The paper summarizes and discusses the developments in research on this topic over the past decade.
Auch kleine und mittlere Unternehmen (KMUs) benötigen zunehmend ein effektives Informationstechnologie- (IT)-Management, um wettbewerbsfähig zu bleiben. Im Vergleich zu großen Unternehmen verfügen KMUs jedoch oft nicht über die Ressourcen, die Arbeitgeberattraktivität oder den Bedarf, um einen Chief Information Officer (CIO) in Vollzeit zu beschäftigen. Um diese Lücke zu schließen, hat eine wachsende Zahl von Expertinnen und Experten weltweit damit begonnen, CIO-Dienste in Teilzeit anzubieten. Auf diese Weise erhalten KMUs Zugang zu erfahrenen und kompetenten IT-Führungskräften zu einem Bruchteil der Kosten und ohne langfristige Verpflichtungen. Während diese so genannten „Fractional CIOs“ in der Praxis bereits einen Mehrwert schaffen, gibt es noch kaum wissenschaftliche Untersuchungen zu diesem neuen Phänomen. In einem größeren Forschungsprojekt mit insgesamt 62 Fractional CIOs aus 10 Ländern wurden daher eine Definition, Typen verschiedenartiger Engagements und Erfolgsfaktoren abgeleitet. Die vorliegende Studie fasst die Ergebnisse zusammen und setzt sie in Bezug zum deutschen Markt, indem sie drei Fractional CIOs/CTOs aus Deutschland befragt. Es zeigt sich, dass die folgenden vier Engagement-Typen von Fractional CIOs für KMUs in verschiedenen Situationen von Nutzen sind: Strategisches IT-Management, Restrukturierung, Skalierung und Hands-on Support. Darüber hinaus zeigt die Studie, dass Vertrauen, die Unterstützung durch das Top-Management-Team und die Integrität des Fractional CIOs Schlüsselfaktoren für den Erfolg von Fractional CIO-Engagements sind. Für den deutschen Markt werden die Ergebnisse durch drei befragte Fractional CIOs/CTOs weitgehend bestätigt. Die Fractional CIOs/CTOs können zwar keine genauen Gründe für die geringe Akzeptanz der Rolle nennen, betonen aber ihr Wertpotenzial für den deutschen Markt.
The average tenure of Chief Information Officers (CIOs) has increased over the past few years. Nevertheless, the average tenure of CIOs is shorter than that of Chief Executive Officers (CEOs). While most studies on tenure and background are based on data from US IT executives, studies on German CIOs are missing. This study analyzes the tenure of German CIOs as a proxy for management effectiveness and how certain factors influence it. An original and unique dataset of 384 IT executives from German companies is examined. The data include the size and industry sector of the companies, educational and professional backgrounds of the CIOs, and the CIOs' reporting lines. Data were analyzed using the chi-square test and Fisher's exact test. The German CIOs had a median tenure of 4.0 years. However, if we examine executives who are currently in office and executives with a completed term of office separately , the median tenure differs. The results also show that German CIOs do not have shorter tenures than German CEOs. When compared with US CIOs, the results depend on the values selected for comparison. In addition, the analysis shows that neither the size and industry sector of the companies nor the educational and professional backgrounds of the CIOs and managers of the CIO reports have a statistically significant influence on the tenure of IT executives. The factors examined in this study can be considered as preconditions for the CIO position. In the future, factors that play a role during tenure should be examined.
Im Rahmen eines Forschungsprojektes soll eine Plattform für die Vermittlung von Telekonsilen und die Bereitstellung einer Konsilakte an die Telematik-Infrastruktur (TI) angeschlossen werden. Um sowohl eine bestmögliche Skalierbarkeit als auch eine optimale Integrierbarkeit in bestehende Systeme und Anwendungen zu erreichen, wurde HL7 FHIR als syntaktischer Standard für das Reha-Konsil festgelegt. Dieses Dokument liefert einen systematischen Überblick über die notwendigen Schritte und Voraussetzungen, um diesen Anschluss zu bewerkstelligen.
Preface QDSM
(2023)
The first international workshop on Quantum Data Science and anagement (QDSM), co-located with VLDB 2023, is centered around addressing the possibilities of quantum computing for data science and data management. Quantum computing is a relatively new and emerging field that is believed to have huge computational potential in the future. In the QDSM workshop, we want to provide a venue for discussing and publishing novel results of applying quantum computing to hard data science and data management problems. These problems include join order optimization, designing efficient quantum feature maps, studying possibilities of solving linear programs with quantum algorithms, and divergent index tuning with quantum machine learning. Besides, we include a short and visionary survey on quantum computing for databases. Theworkshop provides a platform for active discussion on these and related topics.
Recent advances in the manufacture of quantum computers attract much attention over a wide range of fields, as early-stage quantum processing units (QPU) have become accessible. While contemporary quantum machines are very limited in size and capabilities, mature QPUs are speculated to eventually excel at optimisation problems. This makes them an attractive technology for database problems, many of which are based on complex optimisation problems with large solution spaces. Yet, the use of quantum approaches on database problems remains largely unexplored. In this paper, we address the long-standing join ordering problem, one of the most extensively researched database problems. Rather than running arbitrary code, QPUs require specific mathematical problem encodings. An encoding for the join ordering problem was recently proposed, allowing first small-scale queries to be optimised on quantum hardware. However, it is based on a faithful transformation of a mixed integer linear programming (MILP) formulation for JO, and inherits all limitations of the MILP method. Most strikingly, the existing encoding only considers a solution space with left-deep join trees, which tend to yield larger costs than general, bushy join trees. We propose a novel QUBO encoding for the join ordering problem. Rather than transforming existing formulations, we
construct a native encoding tailored to quantum systems, which allows us to process general bushy join trees. This makes the
full potential of QPUs available for solving join order optimisation problems.
Inverse problems are inherently ill-posed and therefore require regularization techniques to achieve a stable solution. While traditional variational methods have wellestablished theoretical foundations, recent advances in machine learning based approaches have shown remarkable practical performance. However, the theoretical foundations of learning-based methods in the context of regularization are still underexplored. In this paper, we propose a general framework that addresses the current gap between learning-based methods and regularization strategies. In particular, our approach emphasizes the crucial role of data consistency in the solution of inverse problems and introduces the concept of data-proximal null-space networks as a key component for their solution. We provide a complete convergence analysis by extending the concept of regularizing null-space networks with data proximity in the visual part. We present numerical results for limited-view computed tomography to illustrate the validity of our framework.
Ziel der Studie:
Ziel der Studie ist die Messung des Stands der Digitalisierung und die mit einer Anbindung an die Telematikinfrastruktur verbundenen Chancen und Herausforderungen für Rehabilitationseinrichtungen.
Methodik:
Teilstandardisierte Online-Befragung bei Trägern von Rehabilitationseinrichtungen in Bayern (n=33). Der Fragebogen mit 36 Fragen beinhaltet eine leicht veränderte Skala auf Basis des „Electronic Medical Record Adoption Model (EMRAM)“.
Ergebnisse:
Der Digitalisierungsgrad wurde in 70 Prozent der Rehabilitationseinrichtungen mit Stufe 0 angegeben (Stufenmodell bis 7). Die Übermittlung patientenbezogener Daten (Eingang und Ausgang) erfolgt häufig analog, wohingegen die Verarbeitung innerhalb der Einrichtung in vielen Fällen bereits überwiegend digital ist. Beim Anschluss an die Telematikinfrastruktur wird hoher Aufwand bei der Installation, aber auch der Schulung des Personals und der Anpassung der Arbeitsorganisation gesehen.
Schlussfolgerung:
Durch Änderung der gesetzlich-finanziellen Lage in Deutschland eröffnen sich für Rehabilitationseinrichtungen neue Möglichkeiten einer verstärkten Digitalisierung. Hürden hängen mit Anforderungen an IT-Sicherheit, Schulung des Personals und sowie dem ebenfalls geringen Digitalisierungsstand bei Krankenhäusern und Ärzt*innen sowie Patient*innen zusammen, die eine digitale Datenübermittlung erschweren.
The Internet of Things (IoT) is an emerging computing paradigm providing new approaches to collect and analyze environmental data. However, as specific challenges arose, the paradigm of Edge Computing with its potential solution capabilities came into place. The combination of both paradigms is currently highly discussed in industry and research. This paper aims to contribute to this field by conducting a systematic literature review to examine the differences and relation between IoT and Edge Computing on a meta-level. It first investigates conceptual backgrounds, use cases, and implementation types. After that, the differences between the paradigms are highlighted. It becomes clear that the significant distinction is in the architectural composition. However, the scientific consensus reveals that both paradigms have a common historical background, and Edge Computing is perceived as the next step in the evolution of IoT. Furthermore, Edge Computing-based systems can address common IoT challenges identified in the two paradigms’ problem-solution space. Ultimately, there is a need for further research in security, edge intelligence, and standardization, with Edge Computing frameworks able to address these in practice.
Lean IT
(2023)
Companies have applied Lean Management and its methods in their production functions for several decades. They also increasingly use Lean Management to improve service delivery, for example, in their IT organizations, which is referred to as “Lean IT”. Lean IT finds widespread recognition in business practice, but corresponding academic research is still scarce. The paper at hand intends to shed light on the current perspectives of Lean IT from an academic point and a practitioner point of view. The paper applies an innovative quantitative approach of literature analysis using semantic entity annotator and a keyword analysis to systematically identify and compare topics academics and practitioners deem relevant in context of Lean IT. We analyze practitioner media and scholarly articles published from January 2014 to June 2019. The analysis shows that research does not seem to adequately address the topics that are highly relevant for practitioners when it comes to Lean IT, e.g., issues pertinent to Automation, DevOps, role of the CIO, IT Service Management or Scrum in context of Lean IT are under-researched. Our analysis further shows that interest in Lean IT as a field is rising in both groups. Our study can help to guide further research activities.
Business process improvement (BPI) is of high priority for practitioners. But especially the most value-adding phase in a BPI project, namely the “act of improvement”, is insufficiently supported despite the many existing methods and techniques. Until now, it is largely unclear as to what degree existing BPI techniques support each other and are interrelated with one another. Thus, the purpose of this paper is to investigate the functional interdependencies between BPI techniques to get a better understanding for the beneficial synergies between the BPI techniques and to provide a basis for purposefully combining them within projects. Based on the functional interdependencies, a graphical “Functional Interdependency Map” is developed and its usability demonstrated in an experiment. The paper is valuable for academics and practitioners alike because the impact of BPI on organizational performance is high.
MongoDB kompakt
(2023)
Die Dokumentendatenbank MongoDB ist das am weitesten verbreitete NoSQL-Datenbanksystem. Dieses kompakte Werk präsentiert MongoDB von der Installation bis zur Administrierung in großen Rechenclustern. Sie erfahren, wie Sie JSON-Dokumente in Kollektionen einfügen, suchen, ändern und löschen, wie man Replikation und Sharding effektiv einsetzt und wie Sie komplexe Analysen mithilfe der Aggregation-Pipeline durchführen können.
Nowadays, most database lectures are performed with an accompanying visual presentation that further illustrates the conveyed facts. Conventional presentation software allows dynamic elements up to a certain level, for example revealing or changing parts of the slide step by step, or even an interaction with the viewers by means of polls or similar mechanisms. Recently, HTML-and browser-based frameworks for presentations have emerged, which allow an even higher degree of flexibility due to the manifold possibilities of HTML5 and JavaScript. This paper presents an approach of how to interactively modify parts of a slide during the presentation, like SQL-based queries or program code snippets, and show the results pretty-printed on the corresponding slide in real-time. This enables the lecturer to easily show more examples, and answer and illustrate side questions, which they did not prepare in advance.
Typical Alexa skills and other add-ons for voice assistants need to be custom developed for their one specific use case. This paper presents an approach to map arbitrary data sources (databases, APIs, services) to the relational model by using SQL/MED and to transform voice-based queries into SQL. The key challenges for such a universal skill are to correctly map the natural-language question into a SQL query on the correct source table in the federated database and to convert the result set back to a compact and well-understandable answer.
As machine learning becomes ever more popular, the question of how to enable it on ever more platforms becomes more important. This thesis explores the extension of SQL and extension databases for machine learning.
For this purpose, a framework for extending the Exasol database for machine learning is created. This framework uses the existing support for scripting languages and the in-database file system of the Exasol database to integrate existing machine-learning libraries into SQL. Currently, only Scikit-Learn was integrated.
The focuses of our framework are simplicity, usability, smooth integration in SQL, and expressive power, while runtime efficiency and speed are only secondary focuses. Further benefits of the framework are reduction of communication overhead, increased data security, simplification of data synchronization, and the usage of core database strengths. Compared to other in-database machine learning approaches, Apache MADlib and Oracle Machine Learning, our framework most likely has inferior speed and efficiency, while having the advantage of using well-integrated and tested libraries.
This thesis also provides an overview of related work on extending SQL and databases for machine learning. Furthermore, future directions for our framework are discussed.
The created framework is freely available at https://github.com/christoph-grossmann/Exasol_DB_ML_Framework.
Computing a sample mean of time series under dynamic time warping is NP-hard. Consequently, there is an ongoing research effort to devise efficient heuristics. The majority of heuristics have been developed for the constrained sample mean problem that assumes a solution of predefined length. In contrast, research on the unconstrained sample mean problem is underdeveloped. In this article, we propose a generic average-compress (AC) algorithm to address the unconstrained problem. The algorithm alternates between averaging (A-step) and compression (C-step). The A-step takes an initial guess as input and returns an approximation of a sample mean. Then the C-step reduces the length of the approximate solution. The compressed approximation serves as initial guess of the A-step in the next iteration. The purpose of the C-step is to direct the algorithm to more promising solutions of shorter length. The proposed algorithm is generic in the sense that any averaging and any compression method can be used. Experimental results show that the AC algorithm substantially outperforms current state-of-the-art algorithms for time series averaging.
The use of quantum processing units (QPUs) promises speed-ups for solving computational problems. Yet, current devices are limited by the number of qubits and suffer from significant imperfections, which prevents achieving quantum advantage. To step towards practical utility, one approach is to apply hardware-software co-design methods. This can involve tailoring problem formulations and algorithms to the quantum execution environment, but also entails the possibility of adapting physical properties of the QPU to specific applications. In this work, we follow the latter path, and investigate how key figures— circuit depth and gate count—required to solve four cornerstone NP-complete problems vary with tailored hardware properties. Our results reveal that achieving near-optimal performance and properties does not necessarily require optimal quantum hardware, but can be satisfied with much simpler structures that can potentially be realised for many hardware approaches.m Using statistical analysis techniques, we additionally identify an underlying general model that applies to all subject problems. This suggests that our results may be universally applicable to other algorithms and problem domains, and tailored QPUs can find utility outside their initially envisaged problem domains.
The substantial possible improvements nonetheless highlight the importance of QPU tailoring to progress towards practical deployment and scalability of quantum software.
Quantum computing is a relatively new paradigm that has raised considerable interest in physics and computer science in general but has so far received little attention in software engineering and architecture. Hybrid applications that consist of both quantum and classical components require the development of appropriate quantum software architectures. However, given that quantum software engineering (QSE) in general is a new research area, quantum software architecture–a subresearch area in QSE is also understudied. The goal of this chapter is to provide a list of research challenges and opportunities for such architectures. In addition, to make the content understandable to a broader computer science audience, we provide a brief overview of quantum computing and explain the essential technical foundations.
Quantum computers promise considerable speedups over classical approaches, which has raised interest from many disciplines. Since any currently available implementations suffer from noise and imperfections, achieving concrete speedups for meaningful problem sizes remains a major challenge. Yet, imperfections and noise may remain present in quantum computing for a long while. Such limitations play no role in classical software computing, and software engineers are typically not well accustomed to considering such imperfections, albeit they substantially influence core properties of software and systems. In this paper, we show how to model imperfections with an approach tailored to (quantum) software engineers. We intuitively illustrate, using numerical simulations, how imperfections influence core properties of quantum algorithms on NISQ systems, and show possible options for tailoring future NISQ machines to improve system performance in a co-design approach. Our results are obtained from a software framework that we provide in form of an easy-to-use reproduction package. It does not require computer scientists to acquire deep physical knowledge on noise, yet provide tangible and intuitively accessible means of interpreting the influence of noise on common software quality and performance indicators.
Design propositions for nudging in healthcare: Adoption of national electronic health recordsystems
(2023)
Objectives: Electronic health records (EHRs) are considered important for improving efficiency and reducing costs of ahealthcare system. However, the adoption of EHR systems differs among countries and so does the way the decision to par-ticipate in EHRs is presented. Nudging is a concept that deals with influencing human behaviour within the research streamof behavioural economics. In this paper, we focus on the effects of the choice architecture on the decision for the adoption ofnational EHRs. Our study aims to link influences on human behaviour through nudging with the adoption of EHRs to inves-tigate how choice architects can facilitate the adoption of national information systems.
Methods: We employ a qualitative explorative research design, namely the case study method. Using theoretical sampling,we selected four cases (i.e., countries) for our study: Estonia, Austria, the Netherlands, and Germany. We collected and ana-lyzed data from various primary and secondary sources: ethnographic observation, interviews, scientific papers, homepages,press releases, newspaper articles, technical specifications, publications from governmental bodies, and formal studies.
Results: The findings from our European case studies show that designing for EHR adoption should encompass choice archi-tecture elements (i.e., defaults), technical elements (i.e., choice granularity and access transparency), and institutional ele-ments (i.e., regulations for data protection, information campaigns, and financial incentives) in combination.
Conclusions: Our findings provide insights on the design of the adoption environments of large-scale, national EHR systems.Future research could estimate the magnitude of effects of the determinants.
We present an industrial end-user perspective on the current state of quantum computing hardware for one specific technological approach, the neutral atom platform. Our aim is to assist developers in understanding the impact of the specific properties of these devices on the effectiveness of algorithm execution. Based on discussions with different vendors and recent literature, we discuss the performance data of the neutral atom platform. Specifically, we focus on the physical qubit architecture, which affects state preparation, qubit-to-qubit connectivity, gate fidelities, native gate instruction set, and individual qubit stability. These factors determine both the quantum-part execution time and the end-to-end wall clock time relevant for end-users, but also the ability to perform fault-tolerant quantum computation in the future. We end with an overview of which applications have been shown to be well suited for the peculiar properties of neutral atom-based quantum computers.
In a variety of tomographic applications, data cannot be fully acquired, leading to severely underdetermined image reconstruction. Conventional methods result in reconstructions with significant artifacts. In order to remove these artifacts, regularization methods have to be applied that incorporate additional information. An important example is TV reconstruction which is well known to efficiently compensate for missing data and well reduces reconstruction artifacts. At the same time, however, tomographic data is also contaminated by noise, which poses an additional challenge. The use of a single regularizer within a variational regularization framework must therefore account for both the missing data and the noise. However, a single regularizer may not be ideal for both tasks. For example, the TV regularizer is a poor choice for noise reduction over different scales, in which case ℓ1 curvelet regularization methods work well. To address this issue, in this paper we introduce a novel variational regularization framework that combines the advantages of two different regularizers. The basic idea of our framework is to perform reconstruction in two stages, where the first stage mainly aims at accurate reconstruction in the presence of noise, and the second stage aims at artifact reduction. Both reconstruction stages are connected by a data proximity condition. The proposed method is implemented and tested for limited-view CT using a combined curvelet-TV approach. We define and implement a curvelet transform adapted to the limited view problem and demonstrate the advantages of our approach in a series of numerical experiments in this context.
We study samples with full and partial occlusion causing streak artifacts, and propose two mod-ifications of filtered backprojection for artifact removal. Data is obtained by the SPring-8 synchrotron using a monochromatic parallel-beam scan [1]. Thresholding in the sinogram segments the metal, resulting in edges on which we apply 1) a smooth transition, or 2) a Dirichlet boundary condition.
The transfer of knowledge from client to service provider poses major challenges in information systems (IS) offshoring projects. Knowledge transfer directly affects IS offshoring success. Therefore, associated challenges must be overcome. Our study examines the determinants of success and failure of knowledge transfer in IS offshoring projects based on a ranking-type Delphi study. We questioned 32 experts from Germany, each with more than ten years of experience in near- or offshore initiatives to seek a consensus among them. We identified 19 success and 20 failure determinants. These determinants are ranked in order of importance using best-worst scaling. Aspects of closer cooperation are critical for effective knowledge transfer. This includes regular collaboration, willingness to help and support, and mutual trust. In contrast, critical determinants of failure are concerned with fears and fluctuation of human resources. Hidden ambiguities or knowledge gaps, an unwillingness and disability to share knowledge, and high fluctuation of human resources negatively impact knowledge transfer.
Organizations are under increasing pressure to develop applications within budget and time at high quality. Therefore, multiple organizations adopt Low Code Development Platforms (LCDP) to develop applications faster and cheaper compared to traditional application development. However, current research on LCDP adoption lacks empirical grounding as well as a deeper understanding of the importance of adoption drivers and inhibitors. We conducted semi-structured interviews and a Delphi study with seventeen experts to address these gaps. As a result, we identified twelve drivers and nineteen inhibitors for adopting LCDPs. We show that the experts have a consensus on the most and the least important drivers and inhibitors for LCDP adoption. Yet, the ranking of the drivers and inhibitors between the most and least important is highly context dependent. For some drivers and inhibitors, the experts’ ranking is similar to academic literature, whereas, for others, it differs. In conclusion, the study at hand empirically validates drivers and inhibitors for LCDP adoption, adds six new drivers and six new inhibitors to the body of knowledge, and analyses the importance of these factors.
In this paper, we present a new approach to determine the estimated time of arrival (ETA) for bus routes using (Deep) Graph Convolutional Networks (DGCNs). In addition we use the same DGCN to detect detours within a route. In our application, a classification of routes and their underlying graph structure is performed using Graph Learning. Our model leads to a fast prediction and avoids solving the vehicle routing problem (VRP) through expensive computations. Moreover, we describe how to predict travel time for all routes using the same DGCN Model. This method makes it possible not to use a more computationally intensive approximation algorithm when determining long travel times with many intermediate stops, but to use our network for an early estimate of the quality of a route. Long travel times, in our case result from the use of a call-bus system, which must distribute many passengers among several vehicles and can take them to places without a regular stop. For a case study, the rural town of Roding in Bavaria is used. Our training data for this area results from an approximation algorithm that we implemented to optimize routes, and to generate an archive of routes of varying quality simultaneously.
Quantum Machine Learning: Foundation, New Techniques, and Opportunities for Database Research
(2023)
In the last few years, the field of quantum computing has experienced remarkable progress. The prototypes of quantum computers already exist and have been made available to users through cloud services (e.g., IBM Q experience, Google quantum AI, or Xanadu quantum cloud). While fault-tolerant and large-scale quantum computers are not available yet (and may not be for a long time, if ever), the potential of this new technology is undeniable. Quantum algorithms havethe proven ability to either outperform classical approaches for several tasks, or are impossible to be efficiently simulated by classical means under reasonable complexity-theoretic assumptions. Even imperfect current-day technology is speculated to exhibit computational advantages over classical systems. Recent research is using quantum computers to solve machine learning tasks. Meanwhile, the database community already successfully applied various machine learning algorithms for data management tasks, so combining the fields seems to be a promising endeavour. However, quantum machine learning is a new research field for most database researchers. In this tutorial, we provide a fundamental introduction to quantum computing and quantum machine learning and show the potential benefits and applications for database research. In addition, we demonstrate how to apply quantum machine learning to the optimization of join order problem for databases.
Ascertaining reproducibility of scientific experiments is receiving increased attention across disciplines. We argue that the necessary skills are important beyond pure scientific utility, and that they should be taught as part of software engineering (SWE) education. They serve a dual purpose: Apart from acquiring the coveted badges assigned to reproducible research, reproducibility engineering is a lifetime skill for a professional industrial career in computer science.
SWE curricula seem an ideal fit for conveying such capabilities, yet they require some extensions, especially given that even at flagship conferences like ICSE, only slightly more than one-third of the technical papers (at the 2021 edition) receive recognition for artefact reusability. Knowledge and capabilities in setting up engineering environments that allow for reproducing artefacts and results over decades (a standard requirement in many traditional engineering disciplines), writing semi-literate commit messages that document crucial steps of a decision-making process and that are tightly coupled with code, or sustainably taming dynamic, quickly changing software dependencies, to name a few: They all contribute to solving the scientific reproducibility crisis, and enable software engineers to build sustainable, long-term maintainable, software-intensive, industrial systems. We propose to teach these skills at the undergraduate level, on par with traditional SWE topics.
Finite state machines (FSMs) are an appealing mechanism for simple practical computations: They lend themselves to very effcient and deterministic implementation, are easy to understand, and allow for formally proving many properties of interest. Unfortunately, their computational power is deemed insuffcient for many tasks, and their usefulness has been further hampered by the state space explosion problem and other issues when naïvely trying to scale them to sizes large enough for many real–life applications.
This paper expounds on theory and implementation of multiple coupled fnite state machines (McFSMs), a novel mechanism that combines benefits of FSMs with near Turing-complete, practical computing power, and that was designed from the ground up to support static analysis and reasoning. We develop an elaborate category–theoretical foundation based on non–deterministic Mealy machines, which gives a suitable algebraic description for novel ways of blending di#erent computing models. Our experience is based on a domain specific language and an integrated development environment that can compile McFSM models to multiple target languages, applying it to use-cases based on industrial scenarios. We discuss properties and advantages of McFSMs, explain how the mechanism can interact with real–world systems and existing code without sacrificing provability, determinism or performance.
We discuss how McFSMs can be used to replace and improve on commonly employed programming patterns, and show how their effcient handling of large state spaces enables them to be used as core building blocks for distributed, safety critical, and real–time systems of industrial complexity, which contributes to the longdesired goal of providing executable specifications.
Computer-based automation in industrial appliances led to a growing number of logically dependent, but physically separated embedded control units per appliance. Many of those components are safety-critical systems, and require adherence to safety standards, which is inconsonant with the relentless demand for features in those appliances. Features lead to a growing amount of control units per appliance, and to a increasing complexity of the overall software stack, being unfavourable for safety certifications. Modern CPUs provide means to revise traditional separation of concerns design primitives: the consolidation of systems, which yields new engineering challenges that concern the entire software and system stack.
Multi-core CPUs favour economic consolidation of formerly separated systems with one efficient single hardware unit. Nonetheless, the system architecture must provide means to guarantee the freedom from interference between domains of different criticality. System consolidation demands for architectural and engineering strategies to fulfil requirements (e.g., real-time or certifiability criteria) in safety-critical environments.
In parallel, there is an ongoing trend to substitute ordinary proprietary base platform software components by mature OSS variants for economic and engineering reasons. There are fundamental differences of processual properties in development processes of OSS and proprietary software. OSS in safety-critical systems requires development process assessment techniques to build an evidence-based fundament for certification efforts that is based upon empirical software engineering methods.
In this thesis, I will approach from both sides: the software and system engineering perspective. In the first part of this thesis, I focus on the assessment of OSS components: I develop software engineering techniques that allow to quantify characteristics of distributed OSS development processes. I show that ex-post analyses of software development processes can be used to serve as a foundation for certification efforts, as it is required for safety-critical systems.
In the second part of this thesis, I present a system architecture based on OSS components that allows for consolidation of mixed-criticality systems on a single platform. Therefore, I exploit virtualisation extensions of modern CPUs to strictly isolate domains of different criticality. The proposed architecture shall eradicate any remaining hypervisor activity in order to preserve realtime capabilities of the hardware by design, while guaranteeing strict isolation across domains.
Die Konformitätsanalyse ist eine Technik der statischen Code-Analyse (SCA) zur Software-Qualitätssicherung. Ihr Kernproblem ist, dass Werkzeuge nicht aus bereits eingetretenen Fehlern automatisiert dazulernen. Zur Lösung wurde in dieser Arbeit das maschinelle Lernen (ML) evaluiert, indem ein wissenschaftlich fundierter und praktisch erprobter Ansatz zur unüberwachten Lerntechnik angewandt und das Ergebnis analysiert wurde. Es wurde festgestellt, dass zur Anwendung auf verschiedene Programmiersprachen nur ein sprachspezifisches API Mining-Tool notwendig ist. Ein derartiges Tool durchsucht in parallelisierter Form Codezeilen und normalisiert sie für maschinelle Lernprozesse. Dieses System wurde für die Programmiersprache C# implementiert, da viele Industrieprojekte in dieser Sprache entwickelt werden. Zur funktionalen Validierung wurde in einer Fallstudie gezeigt, dass Regeln mit einem positiven Effekt auf Software-Qualität gelernt wurden. Konkret wurde der Wartungsaufwand eines Code-Smells in einem Beispielprojekt durch das Auslagern einer gelernten Assoziation in eine gemeinsame Methode um den Faktor 30 reduziert. Die Laufzeit des Algorithmus wurde empirisch in acht open-source Repositorys evaluiert. Durch Parallelisierung kann eine durchschnittliche Laufzeitverbesserung von 45,16% erwartet werden. Allerdings wurden bei der Anwendung auch Grenzen deutlich: Viele Assoziationen sind nutzlos, die Regelbewertung ist von einem subjektiven Faktor abhängig und die Wirtschaftlichkeit des Tools ist deshalb nicht transparent. Dennoch belegt diese Arbeit, dass ein ML-basiertes SCA-Tool als ergänzende Qualitätssicherungsmaßnahme im Software-Engineering möglich ist.
Digitale Transformation in Echtzeit: Die Ziele von morgen basierend auf dem Datenmodell von gestern
(2022)
Die Digitale Transformation fordert Unternehmen aller Couleur. Ironischer Weise sind es gerade die bisher verwendeten IT-Systeme mit ihren starren Strukturen, die Unternehmen in Ihrer digitalen Trans-formation oft ausbremsen. Auch wenn die Soft-warehersteller längst reagiert haben und neue, flexib-lere Versionen ihrer Produkte anbieten, so ist ein größerer Softwarewechsel immer noch eine Heraus-forderung für Unternehmen und ein Schritt der wohl-überlegt und geplant sein will. In dieser Arbeit wird deshalb ein Vorgehen vorge-stellt, um mittels In-Memory Technologie und Vir-tualisierung zumindest die wichtigsten Ergebnisse der Transformation bereits auf den bestehenden Da-tenmodellen in Echtzeit zu generieren. Dadurch wird genug Zeit gewonnen, um die eigentliche Transfor-mation der IT-Landschaft geplant und mit der not-wendigen Sorgfalt durchzuführen.
The prospect of achieving computational speedups by exploiting quantum phenomena makes the use of quantum processing units (QPUs) attractive for many algorithmic database problems. Query optimisation, which concerns problems that typically need to explore large search spaces, seems like an ideal match for the known quantum algorithms. We present the first quantum implementation of join ordering, which is one of the most investigated and fundamental query optimisation problems, based on a reformulation to quadratic binary unconstrained optimisation problems. We empirically characterise our method on two state-of-the-art approaches (gate-based quantum computing and quantum annealing), and identify speed-ups compared to the best know classical join ordering approaches for input sizes that can be processed with current quantum annealers. However, we also confirm that limits of early-stage technology are quickly reached.
Current QPUs are classified as noisy, intermediate scale quantum computers (NISQ), and are restricted by a variety of limitations that reduce their capabilities as compared to ideal future quantum computers, which prevents us from scaling up problem dimensions and reaching practical utility. To overcome these challenges, our formulation accounts for specific QPU properties and limitations, and allows us to trade between achievable solution quality and possible problem size.
In contrast to all prior work on quantum computing for query optimisation and database-related challenges, we go beyond currently available QPUs, and explicitly target the scalability limitations: Using insights gained from numerical simulations and our experimental analysis, we identify key criteria for co-designing QPUs to improve their usefulness for join ordering, and show how even relatively minor physical architectural improvements can result in substantial enhancements. Finally, we outline a path towards practical utility of custom-designed QPUs.
We evaluate the applicability of quantum computing on two fundamental query optimization problems, join order optimization and multi query optimization (MQO). We analyze the problem dimensions that can be solved on current gate-based quantum systems and quantum annealers, the two currently commercially available architectures.
First, we evaluate the use of gate-based systems on MQO, previously solved with quantum annealing. We show that, contrary to classical computing, a different architecture requires involved adaptations. We moreover propose a multi-step reformulation for join ordering problems to make them solvable on current quantum systems. Finally, we systematically evaluate our contributions for gate-based quantum systems and quantum annealers. Doing so, we identify the scope of current limitations, as well as the future potential of quantum computing technologies for database systems.
The design of the NoSQL schema has a direct impact on the scalability of web applications. Especially for developers with little experience in NoSQL stores, the risks inherent in poor schema design can be incalculable. Worse yet, the issues will only manifest once the application has been deployed, and the growing user base causes highly concurrent writes. In this paper, we present a model checking approach to reveal scalability bottlenecks in NoSQL schemas. Our approach draws on formal methods from tree automata theory to perform a conservative static analysis on both the schema and the expected write-behavior of users. We demonstrate the impact of schema-inherent bottlenecks for a popular NoSQL store, and show how concurrent writes can ultimately lead to a considerable share of failed transactions.
Virtualization has come a long way since its beginnings in the 1960s. Nowadays, Virtual Machine Monitor (VMM) - or hypervisor-based virtualization of servers is the de facto standard in data centers and a building block of the cloud hype. In recent years, virtualization has also been adopted to embedded devices such as avionics systems and mobile phones. The first mass deployment of embedded virtualization can probably be seen in video game consoles, though. However, it is still not employed by automotive electronics. This is despite the fact that with the upcoming domain controller architecture, virtualization can yield benefits beyond a mere consolidation of a multitude of Electronic Control Units (ECUs) into a few Domain Controller Units (DCUs). This paper presents merits of automotive virtualization, especially as a foundation for DCUs.
Today, ubiquitous mobile devices have not only arrived but entered the safety critical domain. There, systems are about to be controlled where human health or even human life is put at risk. For example, in automation systems first ideas surface to control parts of the system via a COTS smartphone. Another example is the idea to control the autonomous parking function of a car via a COTS smartphone too. As beneficial and convenient these ideas are on the first thought, on the second thought, dangers of these approaches become obvious. Especially in case of failures the system’s safety has to be maintained. The open question is how to achieve this mandatory requirement with COTS components, e.g. smartphones that are not developed following the development process necessary for safetycritical systems. This paper presents a concept to reliably detect human interaction while activating safety critical functions via COTS mobile devices. Thus a means is provided to detect erroneous activation requests for the safetycritical function.
We present two methods that combine image reconstruction and edge detection in computed tomography (CT) scans. Our first method is as an extension of the prominent filtered backprojection algorithm. In our second method we employ ℓ1-regularization for stable calculation of the gradient. As opposed to the first method, we show that this approach is able to compensate for undersampled CT data.
We present a paradigm for characterization of artifacts in limited data tomography problems. In particular, we use this paradigm to characterize artifacts that are generated in reconstructions from limited angle data with generalized Radon transforms and general filtered backprojection type operators. In order to find when visible singularities are imaged, we calculate the symbol of our reconstruction operator as a pseudodifferential operator.
The performance of cognitive models often depends on the settings of specific model parameters, such as the rate of memory decay or the speed of motor responses. The systematic exploration of a model’s parameter space can yield relevant insights into model behavior and can also be used to improve the fit of a model to human data. However, exhaustive parameter space searches quickly run into a combinatorial explosion as the number of parameters investigated increases. Taking an established instance-based learning task as example, we show
how simulation using parallel computing and derivative-free optimization methods can be applied to investigate the effects
of different parameter settings. We find that both global optimization methods involving genetic algorithms as well as local methods yield satisfactory results in this case. Furthermore, we show how a model implemented in a specific cognitive architecture (ACT-R) can be mathematically reformulated to prepare the application of derivative-based optimization methods which promise further efficiency gains for quantitative analysis.
PURPOSE
Reconstruction of x-ray computed tomography (CT) data remains a mathematically challenging problem in medical imaging. Complementing the standard analytical reconstruction methods, sparse regularization is growing in importance, as it allows inclusion of prior knowledge. The paper presents a method for sparse regularization based on the curvelet frame for the application to iterative reconstruction in x-ray computed tomography.
METHODS
In this work, the authors present an iterative reconstruction approach based on the alternating direction method of multipliers using curvelet sparse regularization.
RESULTS
Evaluation of the method is performed on a specifically crafted numerical phantom dataset to highlight the method's strengths. Additional evaluation is performed on two real datasets from commercial scanners with different noise characteristics, a clinical bone sample acquired in a micro-CT and a human abdomen scanned in a diagnostic CT. The results clearly illustrate that curvelet sparse regularization has characteristic strengths. In particular, it improves the restoration and resolution of highly directional, high contrast features with smooth contrast variations. The authors also compare this approach to the popular technique of total variation and to traditional filtered backprojection.
CONCLUSIONS
The authors conclude that curvelet sparse regularization is able to improve reconstruction quality by reducing noise while preserving highly directional features.
Differential phase contrast imaging (DPCI) enables the visualization of soft tissue contrast using X-rays. In this work we introduce a reconstruction framework based on curvelet expansion and sparse regularization for DPCI. We will show that curvelets provide a suitable data representation for DPCI reconstruction that allows preservation of edges as well as an exact analytic representation of the system matrix. As a first evaluation, we show results using simulated phantom data
This thesis is devoted to the problem of tomographic reconstruction at limited angular range. In the first part, we prove a characterization of filtered backprojection reconstructions from limited angle data. Moreover, we develop a strategy for artifact reduction and stabilization. In the second part, we introduce a new edge-preserving reconstruction algorithm for limited angle tomography and analyze this algorithm mathematically. Some numerical experiments are also presented.
We propose a new framework for limited angle tomographic reconstruction. Our approach is based on the observation that for a given acquisition geometry only a few (visible) structures of the object can be reconstructed reliably using a limited angle data set. By formulating this problem in the curvelet domain, we can characterize those curvelet coefficients which correspond to visible structures in the image domain. The integration of this information into the formulation of the reconstruction problem leads to a considerable dimensionality reduction and yields a speedup of the corresponding reconstruction algorithms.
A Step Towards the Automated Diagnosis of Parkinson's Disease: Analyzing Handwriting Movements
(2015)
Parkinson’s disease (PD) has affected millions of people world-wide, being its major problem the loss of movements and, consequently, the ability of working and locomotion. Although we can find several works that attempt at dealing with this problem out there, most of them make use of datasets composed by a few subjects only. In this work, we present some results toward the automated diagnosis of PD by means of computer vision-based techniques in a dataset composed by dozens of patients, which is one of the main contributions of this work. The dataset is part of a joint research project that aims at extracting both visual and signal-based information from healthy and PD patients in order to go forward the early diagnosis of PD patients. The dataset is composed by handwriting clinical exams that are analyzed by means of image processing and machine learning techniques, being the preliminary results encouraging and promising. Additionally, a new quantitative feature to measure the amount of tremor of an individual’s handwritten trace called Mean Relative Tremor is also presented.
We consider the reconstruction problem for limited angle tomography using filtered backprojection (FBP) and lambda tomography. We use microlocal analysis to explain why the well-known streak artifacts are present at the end of the limited angular range. We explain how to mitigate the streaks and prove that our modified FBP and lambda operators are standard pseudodifferential operators, and so they do not add artifacts. We provide reconstructions to illustrate our mathematical results.
We investigate the reconstruction problem of limited angle tomography. Such problems arise naturally in applications like digital breast tomosynthesis, dental tomography, electron microscopy, etc. Since the acquired tomographic data is highly incomplete, the reconstruction problem is severely ill-posed and the traditional reconstruction methods, e.g. filtered backprojection (FBP), do not perform well in such situations.
To stabilize the reconstruction procedure additional prior knowledge about the unknown object has to be integrated into the reconstruction process. In this work, we propose the use of the sparse regularization technique in combination with curvelets. We argue that this technique gives rise to an edge-preserving reconstruction. Moreover, we show that the dimension of the problem can be significantly reduced in the curvelet domain. To this end, we give a characterization of the kernel of the limited angle Radon transform in terms of curvelets and derive a characterization of solutions obtained through curvelet sparse regularization. In numerical experiments, we will show that the theoretical results directly translate into practice and that the proposed method outperforms classical reconstructions.
Artifacts in Incomplete Data Tomography with Applications to Photoacoustic Tomography and Sonar
(2015)
We develop a paradigm using microlocal analysis that allows one to characterize the visible and added singularities in a broad range of incomplete data tomography problems. We give precise characterizations for photoacoustic and thermoacoustic tomography and sonar, and provide artifact reduction strategies. In particular, our theorems show that it is better to arrange sonar detectors so that the boundary of the set of detectors does not have corners and is smooth. To illustrate our results, we provide reconstructions from synthetic spherical mean data as well as from experimental photoacoustic data.
We investigate the reconstruction problem for limited angle tomography. Such problems arise naturally in applications like digital breast tomosynthesis, dental tomography, etc. Since the acquired tomographic data is highly incomplete, the reconstruction problem is severely ill-posed and the traditional reconstruction methods, such as filtered backprojection (FBP), do not perform well in such situations. To stabilize the inversion we propose the use of a sparse regularization technique in combination with curvelets. We argue that this technique has the ability to preserve edges. As our main result, we present a characterization of the kernel of the limited angle Radon transform in terms of curvelets. Moreover, we characterize reconstructions which are obtained via curvelet sparse regularizations at a limited angular range. As a result, we show that the dimension of the limited angle problem can be significantly reduced in the curvelet domain.
We propose a new algorithmic approach to the non-smooth and non-convex Potts problem (also called piecewise-constant Mumford–Shah problem) for inverse imaging problems. We derive a suitable splitting into specific subproblems that can all be solved efficiently. Our method does not require a priori knowledge on the gray levels nor on the number of segments of the reconstruction. Further, it avoids anisotropic artifacts such as geometric staircasing. We demonstrate the suitability of our method for joint image reconstruction and segmentation. We focus on Radon data, where we in particular consider limited data situations. For instance, our method is able to recover all segments of the Shepp–Logan phantom from seven angular views only. We illustrate the practical applicability on a real positron emission tomography dataset. As further applications, we consider spherical Radon data as well as blurred data.
Background
The purpose of this study was to evaluate the impact of Cone Beam CT (CBCT) based setup correction on total dose distributions in fractionated frameless stereotactic radiation therapy of intracranial lesions.
Methods
Ten patients with intracranial lesions treated with 30 Gy in 6 fractions were included in this study. Treatment planning was performed with Oncentra® for a SynergyS® (Elekta Ltd, Crawley, UK) linear accelerator with XVI® Cone Beam CT, and HexaPOD™ couch top. Patients were immobilized by thermoplastic masks (BrainLab, Reuther). After initial patient setup with respect to lasers, a CBCT study was acquired and registered to the planning CT (PL-CT) study. Patient positioning was corrected according to the correction values (translational, rotational) calculated by the XVI® system. Afterwards a second CBCT study was acquired and registered to the PL-CT to confirm the accuracy of the corrections. An in-house developed software was used for rigid transformation of the PL-CT to the CBCT geometry, and dose calculations for each fraction were performed on the transformed CT. The total dose distribution was achieved by back-transformation and summation of the dose distributions of each fraction. Dose distributions based on PL-CT, CBCT (laser set-up), and final CBCT were compared to assess the influence of setup inaccuracies.
Results
The mean displacement vector, calculated over all treatments, was reduced from (4.3 ± 1.3) mm for laser based setup to (0.5 ± 0.2) mm if CBCT corrections were applied. The mean rotational errors around the medial-lateral, superior-inferior, anterior-posterior axis were reduced from (−0.1 ± 1.4)°, (0.1 ± 1.2)° and (−0.2 ± 1.0)°, to (0.04 ± 0.4)°, (0.01 ± 0.4)° and (0.02 ± 0.3)°. As a consequence the mean deviation between planned and delivered dose in the planning target volume (PTV) could be reduced from 12.3% to 0.4% for D95 and from 5.9% to 0.1% for Dav. Maximum deviation was reduced from 31.8% to 0.8% for D95, and from 20.4% to 0.1% for Dav.
Conclusion
Real dose distributions differ substantially from planned dose distributions, if setup is performed according to lasers only. Thermoplasic masks combined with a daily CBCT enabled a sufficient accuracy in dose distribution.
Re-irradiation of spinal column metastases by IMRT: Impact of setup errors on the dose distribution
(2013)
Background
This study investigates the impact of an automated image guided patient setup correction on the dose distribution for ten patients with in-field IMRT re-irradiation of vertebral metastases.
Methods
10 patients with spinal column metastases who had previously been treated with 3D-conformal radiotherapy (3D-CRT) were simulated to have an in-field recurrence. IMRT plans were generated for treatment of the vertebrae sparing the spinal cord. The dose distributions were compared for a patient setup based on skin marks only and a Cone Beam CT (CBCT) based setup with translational and rotational couch corrections using an automatic robotic image guided couch top (Elekta - HexaPOD™ IGuide® - system). The biological equivalent dose (BED) was calculated to evaluate and rank the effects of the automatic setup correction for the dose distribution of CTV and spinal cord.
Results
The mean absolute value (± standard deviation) over all patients and fractions of the translational error is 6.1 mm (±4 mm) and 2.7° (±1.1 mm) for the rotational error. The dose coverage of the 95% isodose for the CTV is considerable decreased for the uncorrected table setup. This is associated with an increasing of the spinal cord dose above the tolerance dose.
Conclusions
An automatic image guided table correction ensures the delivery of accurate dose distribution and reduces the risk of radiation induced myelopathy.
This paper introduces a novel chaotic flower pollination algorithm (CFPA) to solve a tardiness-constrained flow-shop scheduling problem with simultaneously loaded stations. This industrial manufacturing problem is modeled from a filter basket production line in Germany and has been generally solved using standard deterministic algorithms. This research develops a metaheuristic approach based on the highly efficient flower pollination algorithm coupled with different chaos maps for stochasticity. The objective function targeted is the tardiness constraint of the due dates. Fifteen different experiments with thirty scenarios are generated to mimic industrial conditions. The results are compared with the genetic algorithm and with the four standard benchmark priority rule-based deterministic algorithms of First In First Out, Raghu and Rajendran, Shortest Processing Time and Slack. From the obtained results and analysis of the relative difference, percentage relative difference and t tests, CFPA was found to be significantly better performing than the deterministic heuristics and the GA algorithm.
In this work, a method for reducing the number of degrees of freedom in online optimal dynamic experiment design problems for systems described by differential equations is proposed. The online problems are posed such that only the inputs which extend an operation policy resulting from an experiment designed offline are optimized. This is done by formulating them as multiple experiment designs, considering explicitly the information of the experiment designed offline and possible time delays unknown a priori. The performance of the method is shown for the case of the separation of isopropanolol isomers in a Simulated Moving Bed plant.
Multiple hop routing in mobile ad hoc networks can minimize energy consumption and increase data throughput. Yet, the problem of radio interferences remain. However if the routes are restricted to a basic network based on local neighborhoods, these interferences can be reduced such that standard routing algorithms can be applied.
We compare different network topologies for these basic networks, i.e. the Yao-graph (aka. Θ-graph) and some also known related models, which will be called the SymmYgraph (aka. YS-graph), the SparsY-graph (aka. YY-graph) and the BoundY-graph. Further, we present a promising network topology called the HL-graph (based on Hierarchical Layers).
We compare these topologies regarding degree, spanner-properties, and communication features. We investigate how these network topologies bound the number of (uni- and bidirectional) interferences and whether these basic networks provide energy-optimal or congestion-minimal routing. Then, we compare the ability of these topologies to handle
dynamic changes of the network when radio stations appear and disappear. For this we measure the number of involved radio stations and present distributed algorithms for repairing the network structure.
Computational models of cognition provide an interface to connect advanced mathematical tools and methods to empirically supported theories of behavior in psychology, cognitive science, and neuroscience. In this article, we consider a computational model of instance-based learning, implemented in the ACT-R cognitive architecture. We propose an approach for obtaining mathematical reformulations of such cognitive models that improve their computational tractability. For the well-established Sugar Factory dynamic decision making task, we conduct a simulation study to analyze central model parameters. We show how mathematical optimization techniques can be applied to efficiently identify optimal parameter values with respect to different optimization goals. Beyond these methodological contributions, our analysis reveals the sensitivity of this particular task with respect to initial settings and yields new insights into how average human performance deviates from potential optimal performance. We conclude by discussing possible extensions of our approach as well as future steps towards applying more powerful derivative-based optimization methods.
The goal of this paper is to increase the computation speed of MapReduce jobs by reducing the accuracy of the result. Often, the timely processing is more important than the precision of the result. Hadoop has no built-in functionality for such an approximation technique, so the user has to implement sampling techniques manually.
We introduce an automatic system for computing arithmetic approximations. The sampling is based on techniques from statistics and the extrapolation is done generically. This system is also extended by an incremental component which enables the reuse of already computed results to enlarge the sampling size. This can be used iteratively to further increase the sampling size and also the precision of the approximation. We present a transparent incremental sampling approach, so the developed components can be integrated in the Hadoop framework in a non-invasive manner.
Nonlinear ill-posed problem analysis in model-based parameter estimation and experimental design
(2015)
Discrete ill-posed problems are often encountered in engineering applications. Still, their sound analysis is not yet common practice and difficulties arising in the determination of uncertain parameters are typically not assigned properly. This contribution provides a tutorial review on methods for identifiability analysis, regularization techniques and optimal experimental design. A guideline for the analysis and classification of nonlinear ill-posed problems to detect practical identifiability problems is given. Techniques for the regularization of experimental design problems resulting from ill-posed parameter estimations are discussed. Applications are presented for three different case studies of increasing complexity.
Electric and electronic functionalities increase exponentially in every mobility domain. The automotive in-dustry is confronted with a rising system complexity and several restricting requirements and standards (likeAUTOSAR), in particular to design embedded software for electronic control units. To stand against rampantfunctionalities software units could be restructured according to their affiliation and should not be attached toa certain place. This can be effected by integration on single controllers. On the one hand the system wideamount of hardware controllers could such be limited. On the other hand the workload for integration CPUswill rise. To support this paradigm, multi-core systems can provide enough processing power in an efficientway. This paper shows a first approach to combine automotive functionality on such a single controller.
NoSQL-Datenbanksysteme sind in den letzten Jahren sehr populär geworden, gute Gründe sprechen für ihren Einsatz: Eine attraktive Eigenschaft vieler Systeme ist ihre Schema-Flexibilität, die insbesondere in der agilen Anwendungsentwicklung Vorteile bietet. Durch horizontale Skalierbarkeit ermöglichen NoSQL-Datenbanksysteme eine effiziente Verarbeitung großer Datenmengen. Einige Systeme, die für die Datenhaltung interaktiver Anwendungen konzipiert sind, können zudem hochfrequente Nutzeranfragen bedienen. Diesen Vorteilen stehen eine Reihe von Nachteilen gegenüber, aus denen sich neue Herausforderungen für die Anwendungsentwicklung ergeben: Fehlende Standards bei den Anfragesprachen erschweren die Entwicklung datenbanksystemunabhängiger Anwendungen. Schema-Flexibilität im Datenbankmanagementsystem führt dazu, dass die Verantwortung für das Schema-Management in die Anwendung verlagert wird. Im vorliegenden Beitrag werden wesentliche Herausforderungen identifiziert und Lösungsansätze aus Forschung und Praxis vorgestellt. Dabei liegt der Fokus auf schema-flexiblen NoSQL-Datenbanksystemen, mit einem aggregat-orientierten Datenmodell, d. h. Key-Value Datenbanksysteme, dokumentenorientierten Datenbanksystemen und Column-Family Datenbanksystemen.
NoSQL data stores have become very popular over the last years, as good reasons are justifying their application: One attractive feature of many systems is their schema flexibility, which may be preferable in agile software development projects. Due to their horizontal scalability, NoSQL data stores make it possible to efficiently process large amounts of data. Some systems, designed as data backends for interactive applications, can also manage highly frequent user requests. Apart from these advantages, there are also downsides to NoSQL data stores that create new challenges for software development: Missing standards in query languages make it difficult to build data store independent applications. Schema flexibility in the data store shifts the responsibility for schema management into the application. This article identifies substantial challenges as well as solution statements from research and practice. The focus of our survey is on schema-flexible NoSQL data management systems with an aggregate-oriented data model, i. e., key-value data management systems, as well as document and column family data management systems.
Inverse problems are at the heart of many practical problems such as image reconstruction or nondestructive testing. A characteristic feature is their instability with respect to data perturbations. To stabilize the inversion process, regularization methods must be developed and applied. In this paper, we introduce the concept of filtered diagonal frame decomposition, which extends the classical filtered SVD to the case of frames. The use of frames as generalized singular systems allows a better match to a given class of potential solutions and is also beneficial for problems where the SVD is not analytically available. We show that filtered diagonal frame decompositions yield convergent regularization methods, derive convergence rates under source conditions and prove order optimality. Our analysis applies to bounded and unbounded forward operators. As a practical application of our tools, we study filtered diagonal frame decompositions for inverting the Radon transform as an unbounded operator on L2(R2).
We propose a quantum key distribution scheme which closely matches the performance of a perfect single photon source. It nearly attains the physical upper bound in terms of key generation rate and maximally achievable distance. Our scheme relies on a practical setup based on a parametric downconversion source and present day, nonideal photon-number detection. Arbitrary experimental imperfections which lead to bit errors are included. We select decoy states by classical postprocessing. This allows one to improve the effective signal statistics and achievable distance.
Quantum key distribution is among the foremost applications of quantum mechanics, both in terms of fundamental physics and as a technology on the brink of commercial deployment. Starting from principal schemes and initial proofs of unconditional security for perfect systems, much effort has gone into providing secure schemes which can cope with numerous experimental imperfections unavoidable in real world implementations. In this paper, we provide a comparison of various schemes and protocols. We analyse their efficiency and performance when implemented with imperfect physical components. We consider how experimental faults are accounted for using effective parameters. We compare various recent protocols and provide guidelines as to which components propose best advances when being improved.
We experimentally analyze the complete photon number statistics of parametric down-conversion and ascertain the influence of multimode effects. Our results clearly reveal a difference between single-mode theoretical description and the measured distributions. Further investigations assure the applicability of loss-tolerant photon number reconstruction and prove strict photon number correlation between signal and idler modes.
Every security analysis of quantum-key distribution (QKD) relies on a faithful modeling of the employed quantum states. Many photon sources, such as for instance a parametric down-conversion (PDC) source, require a multimode description but are usually only considered in a single-mode representation. In general, the important claim in decoy-based QKD protocols for indistinguishability between signal and decoy states does not hold for all sources. We derive bounds on the single-photon transmission probability and error rate for multimode states and apply these bounds to the output state of a PDC source. We observe two opposing effects on the secure key rate. First, the multimode structure of the state gives rise to a new attack that decreases the key rate. Second, more contributing modes change the photon number distribution from a thermal toward a Poissonian distribution, which increases the key rate.
Random numbers are a valuable component in diverse applications that range from simulations over gambling to cryptography. The quest for true randomness in these applications has engendered a large variety of different proposals for producing random numbers based on the foundational unpredictability of quantum mechanics4,5,6,7,8,9,10,11. However, most approaches do not consider that a potential adversary could have knowledge about the generated numbers, so the numbers are not verifiably random and unique12,13,14,15. Here we present a simple experimental setup based on homodyne measurements that uses the purity of a continuous-variable quantum vacuum state to generate unique random numbers. We use the intrinsic randomness in measuring the quadratures of a mode in the lowest energy vacuum state, which cannot be correlated to any other state. The simplicity of our source, combined with its verifiably unique randomness, are important attributes for achieving high-reliability, high-speed and low-cost quantum random number generators.
Wer seine Daten mit ansehnlichen und informativen Graphen veranschaulichen möchte, braucht meist viel Geduld. Die R-Erweiterung Ggplot2 bringt System in die Grafik, drückt sich in knappem Quellcode aus und bläst frischen Wind in den Alltag der Datenvisualisierung.
Dynamic Time Warping (DTW) is a well-known similarity measure for time series. The standard dynamic programming approach to compute the DTW distance of two length-n time series, however, requires O(n2) time, which is often too slow for real-world applications. Therefore, many heuristics have been proposed to speed up the DTW computation. These are often based on lower bounding techniques, approximating the DTW distance, or considering special input data such as binary or piecewise constant time series. In this paper, we present a first exact algorithm to compute the DTW distance of two run-length encoded time series whose running time only depends on the encoding lengths of the inputs. The worst-case running time is cubic in the encoding length. In experiments we show that our algorithm is indeed fast for time series with short encoding lengths.
DevOps paradigm is widely used in industry to develop software faster, deploy high quality frequent releases of features by integrating and harmonizing the Development and IT Operations activities.
Industries are taking strategic decisions to remove the barriers that existed between Development and Operational teams by encouraging collaborations among these teams throughout System Development Life Cycle (SDLC). These strategic decisions to implement DevOps paradigm resulted in the development and emergence of large arrays of tool chains to support, monitor, and automate activities of various SDLC stages. In this paper authors attempt to give practical insights on how the using of DevOps can speed up the management, development and deployment process of a simple web application. Widely used DevOps model consisting of eight stages is used to implement the example application. A toolchain consisting of state of arts tools is used at various DevOps stages. A detailed explanation of each tool, including details to their implementation and a short evaluation concludes the study. The results revealed that the usage of DevOps enables to accelerate the development process of web applications, as most steps during the build and testing process can be automated. Especially the outsourcing of operational overhead to an external cloud provider can lead to economic advantages, which will impact the future of software development.
This article provides a mathematical classification of artifacts from arbitrary incom-plete X-ray tomography data when using the classical filtered backprojection algorithm. Usingmicrolocal analysis, we prove that all artifacts arise from points at the boundary of the data set.Our results show that, depending on the geometry of the data set boundary, two types of artifactscan arise: object-dependent and object-independent artifacts. The object-dependent artifacts aregenerated by singularities of the object being scanned and these artifacts can extend all along lines.This is a generalization of the streak artifacts observed in limited angle CT. The article also char-acterizes two new phenomena: the object-independent artifacts are caused only by the geometryof the data set boundary; they occur along lines if the boundary of the data set is not smooth andalong curves if the boundary of the data set is smooth. In addition to the geometric descriptionof artifacts, the article also provides characterizations of their strength in Sobolev scale in certaincases. Moreover, numerical reconstructions from simulated and real data are presented illustratingour theorems.This work is motivated by a reconstruction we present from a synchrotron data set in whichartifacts along lines appeared that were independent of the object.The results of this article apply to a wide range of well-known incomplete data problems, in-cluding limited angle CT and region of interest tomography, as well as to unconventional x-ray CTimaging setups. Some of those problems are explicitly addressed in this article, theoretically and numerically.
The use of quantum processing units (QPUs) promises speed-ups for solving computational problems, but the quantum devices currently available possess only a very limited number of qubits and suffer from considerable imperfections. One possibility to progress towards practical utility is to use a co-design approach: Problem formulation and algorithm, but also the physical QPU properties are tailored to the specific application. Since QPUs will likely be used as accelerators for classical computers, details of systemic integration into existing architectures are another lever to influence and improve the practical utility of QPUs.
In this work, we investigate the influence of different parameters on the runtime of quantum programs on tailored hybrid CPU-QPU-systems. We study the influence of communication times between CPU and QPU, how adapting QPU designs influences quantum and overall execution performance, and how these factors interact. Using a simple model that allows for estimating which design choices should be subjected to optimisation for a given task, we provide an intuition to the HPC community on potentials and limitations of co-design approaches. We also discuss physical limitations for implementing the proposed changes on real quantum hardware devices.
In this paper, we present a new Hybrid Genetic Search (HGS) algorithm for solving the Capacitated Vehicle Routing Problem for Pickup and Delivery (CVRPPD) as it is required for public transport in rural areas. One of the biggest peculiarities here is that a large area has to be covered with as few vehicles as possible. The basic idea of this algorithm is based on a more general version of HGS, which we adopted to solve the CVRPPD in rural areas. It also implements improvements that lead to the acceleration of the algorithm and, thereby, to a faster generation of a fastest route. We tested the algorithm on real road data from Roding, a rural district in Bavaria, Germany. Moreover, we designed an API for converting data from the Openrouteservice, so that our algorithm can be applied on real world examples as well.
On embedded processors that are increasingly equipped with multiple CPU cores, static hardware partitioning is an established means of consolidating and isolating workloads onto single chips. This architectural pattern is suitable for mixed-criticality workloads that need to satisfy both, real-time and safety requirements, given suitable hardware properties. In this work, we focus on exploiting contemporary virtualisation mechanisms to achieve freedom from interference respectively isolation between workloads. Possibilities to achieve temporal and spatial isolation-while maintaining real-time capabilities-include statically partitioning resources, avoiding the sharing of devices, and ascertaining zero interventions of superordinate control structures. This eliminates overhead due to hardware partitioning, but implies certain hardware capabilities that are not yet fully implemented in contemporary standard systems. To address such hardware limitations, the customisable and configurable RISC-V instruction set architecture offers the possibility of swift, unrestricted modifications. We present findings on the current RISC-V specification and its implementations that necessitate interventions of superordinate control structures. We identify numerous issues adverse to implementing our goal of achieving zero interventions respectively zero overhead: On the design level, and especially with regards to handling interrupts. Based on micro-benchmark measurements, we discuss the implications of our findings, and argue how they can provide a basis for future extensions and improvements of the RISC-V architecture.
Over the last years, configurational research has become increasingly popular in the Information Systems (IS) discipline. Researchers value configurational methods like Qualitative Comparative Analysis (QCA) as their application contributes to a better understanding of complex phenomena. QCA helps to uncover interrelations of conditions that lead to an outcome, building on the principles of equifinality, conjunctural causation, and asymmetry. More recently, IS researchers have started to analyze qualitative data, like case study data, with QCA. However, there is a lack of methodological guidance on how to calibrate qualitative data into set membership values for QCA. Therefore, this paper structures methodological steps and the associated options to calibrate qualitative data from an interdisciplinary perspective and critically reviews the observed methodological choices in IS research. This paper also gives recommendations for calibrating qualitative data to support informed methodological choices for future research.
When done right, the use of low code development promises a significant competitive advantage in the software development process for organizations. Thus, multiple vendors have created low code development platforms to ease the use of low code development. However, current research on low code development platforms mainly focuses on the technological aspects of the platforms but not on their adoption. Hence, it remains unclear what drives and inhibits the adoption of low code development platforms. We conducted a literature review and identified thirteen factors that inhibit the adoption and seven factors that drive it. We structure these factors along with the diffusion of innovation framework that helps to disentangle drivers and inhibitors. As a result, we provide an initial explanation of the adoption of low code development platforms. Nevertheless, we conclude that existing research on the adoption of low code development platforms is not specific enough to understand the phenomenon substantially. Further, for some factors (e.g., cost), there is a disagreement in the academic literature on whether they are drivers or inhibitors. Hence, we identify gaps and derive avenues for future research.
We introduce the Merkel Podcast Corpus, an audio-visual-text corpus in German collected from 16 years of (almost) weekly Internet podcasts of former German chancellor Angela Merkel. To the best of our knowledge, this is the first single speaker corpus in the German language consisting of audio, visual and text modalities of comparable size and temporal extent. We describe the methods used with which we have collected and edited the data which involves downloading the videos, transcripts and other metadata, forced alignment, performing active speaker recognition and face detection to finally curate the single speaker dataset consisting of utterances spoken by Angela Merkel. The proposed pipeline is general and can be used to curate other datasets of similar nature, such as talk show contents. Through various statistical analyses and applications of the dataset in talking face generation and TTS, we show the utility of the dataset. We argue that it is a valuable contribution to the research community, in particular, due to its realistic and challenging material at the boundary between prepared and spontaneous speech.
Accepted at LREC 2022
Increasing user participation or changing behavior are key goals when applying gamification. Existing studies in domains such as education, health, and enterprise show that gamification can have a positive impact on meeting these goals. However, there is still a lack of detailed insights into how certain game design elements affect user behavior and motivation. To gain further insight, this paper presents a user study in the field with 20, 000 participants of a mobile e-commerce application over a one-month time period to analyze the impact of gamification in the e-commerce domain and to compare the effectiveness of tangible versus intangible rewards. Results show that gamification has a positive impact in the e-commerce domain. The study also reveals that tangible rewards increase the user activity substantially more than intangible rewards. We further show how tangible rewards affect certain user types and provide a first discussion on the lastingness of these rewards.
When an incremental release of a web application is deployed, the structure of data already persisted in the production database may no longer match what the application code expects. Traditionally, eager schema migration is called for, where all legacy data is migrated in one go. With the growing popularity of schema-flexible NoSQL data stores, lazy forms of data migration have emerged: Legacy entities are migrated on-the-fly, one at-a-time, when they are loaded by the application. In this demo, we present Datalution, a tool demonstrating the merits of lazy data migration. Datalution can apply chains of pending schema changes, due to its Datalog-based internal representation. The Datalution approach thus ensures that schema evolution, as part of continous deployment, is carried out correctly.
Frequency conversion (FC) and type-II parametric down-conversion (PDC) processes serve as basic building blocks for the implementation of quantum optical experiments: type-II PDC enables the efficient creation of quantum states such as photon-number states and Einstein–Podolsky–Rosen (EPR)-states. FC gives rise to technologies enabling efficient atom–photon coupling, ultrafast pulse gates and enhanced detection schemes. However, despite their widespread deployment, their theoretical treatment remains challenging. Especially the multi-photon components in the high-gain regime as well as the explicit time-dependence of the involved Hamiltonians hamper an efficient theoretical description of these nonlinear optical processes. In this paper, we investigate these effects and put forward two models that enable a full description of FC and type-II PDC in the high-gain regime. We present a rigorous numerical model relying on the solution of coupled integro-differential equations that covers the complete dynamics of the process. As an alternative, we develop a simplified model that, at the expense of neglecting time-ordering effects, enables an analytical solution. While the simplified model approximates the correct solution with high fidelity in a broad parameter range, sufficient for many experimental situations, such as FC with low efficiency, entangled photon-pair generation and the heralding of single photons from type-II PDC, our investigations reveal that the rigorous model predicts a decreased performance for FC processes in quantum pulse gate applications and an enhanced EPR-state generation rate during type-II PDC, when EPR squeezing values above 12 dB are considered.
Currently, Parkinson’s Disease (PD) has no cure or accurate diagnosis, reaching approximately 60, 000 new cases yearly and worldwide, being more often in the elderly population. Its main symptoms can not be easily uncorrelated with other illness, being way more difficult to be identified at the early stages. As such, computer-aided tools have been recently used to assist in this task, but the challenge in the automatic identification of Parkinson’s Disease still persists. In order to cope with this problem, we propose to employ Restricted Boltzmann Machines (RBMs) to learn features in an unsupervised fashion by analyzing images from handwriting exams, which aim at assessing the writing skills of potential individuals. These are one of the main symptoms of PD-prone people, since such kind of ability ends up being severely affected. We show that RBMs can learn proper features that help supervised classifiers in the task of automatic identification of PD patients, as well as one can obtain a more compact representation of the exam for the sake of storage and computational load purposes.
We present jHound, a tool for profiling large collections of JSON data, and apply it to thousands of data sets holding open government data. jHound reports key characteristics of JSON documents, such as their nesting depth. As we show, jHound can help detect structural outliers, and most importantly, badly encoded documents: jHound can pinpoint certain cases of documents that use string-typed values where other native JSON datatypes would have been a better match. Moreover, we can detect certain cases of maladaptively structured JSON documents, which obviously do not comply with good data modeling practices. By interactively exploring particular example documents, we hope to inspire discussions in the community about what makes a good JSON encoding.
SQL-on-Hadoop processing engines have become state-of-the-art in data lake analysis. However, the skills required to tune such systems are rare. This has inspired automated tuning advisors which profile the query workload and produce tuning setups for the low-level MapReduce jobs. Yet with highly dynamic query workloads, repeated re-tuning costs time and money in IaaS environments. In this paper, we focus on reducing the costs for up-front tuning. At the heart of our approach is the observation that a SQL query is compiled into a query plan of MapReduce jobs. While the plans differ from query to query, single jobs tend to be similar between queries. We introduce the notion of the code signature of a MapReduce job and, based on this, our concept of job similarity. We show that we can effectively recycle tuning setups from similar MapReduce jobs already profiled. In doing so, we can leverage any third-party tuning adviser for MapReduce engines. We are able to show that by recycling tuning setups, we can reduce the time spent on profiling by 50% in the TPC-H benchmark.
Basierend auf einer systematischen und umfangreichen Analyse von Praxisbeiträgen zum Thema Schatten-IT und einer Interviewstudie mit 16 IT-Führungskräften beschreibt der vorliegende Artikel Governance-Aspekte zu diesem Phänomen. Er ergänzt damit vorhergehende akademische Studien. Es zeigt sich, dass unter Praktikern der Eindruck vorherrscht, dass IT-Abteilungen unter zunehmendem Druck stehen, schneller auf sich ändernde Anforderungen aus den Fachbereichen reagieren zu müssen. Können IT-Abteilungen diesen Erwartungen nicht entsprechen, beschaffen sich Fachbereiche und Nutzer selbst Lösungen in Form von Schatten-IT. Als mögliche Antwort darauf kann sich die IT-Abteilung agiler organisieren und die IT-Architektur im Unternehmen modernisieren. Eine weitere Möglichkeit besteht darin, sich das innovative Potenzial von Schatten-IT zunutze zu machen und deren Umsetzung aktiv durch organisatorische und technische Maßnahmen zu unterstützen. IT-Sicherheitsmanagement und technische Schutzmechanismen können helfen, die so entstandenen Lösungen abzusichern und die Risiken zu minimieren. Die IT-Abteilung könnte sich als Konsequenz aus all diesen Maßnahmen zu einem nutzerorientierten, internen Service-Provider und strategischen Partner für die Fachbereiche entwickeln.
Recent advances in the development of smart homes have led to the availability of a wide variety of devices providing a high level of convenience via gesture and speech control or fully automated operation. Many smart home appliances also address the aspects of safety and electricity savings by automatically powering themselves off after not being used for a while. However, many devices remain in a typical household that are not themselves "smart", or are not primarily electric (such as heating systems). We address the savings aspect by identifying processes involving the use of multiple devices in the electrical flow data, as captured by a smart meter in a modern household, rather than focusing on a single appliance. Therefore, we introduce a novel approach to usage pattern analysis based on the idea that a pattern of device usages as a result of a resident's 'routine' (such as making breakfast) can be interpreted similarly to a natural language 'sentence'; Natural Language Processing (NLP) algorithms can then be used for interpreting the residents' behavior. We introduce the notion of bag-of-devices (BoD), derived from the bag-of-words model used in document classification. In an experiment, we show how we use this model to infer predictions about the inhabitants from device usage, such as the resident leaving for the day or just to fetch the newspaper.
Automotive Original Equipment Manufacturer (OEM) and suppliers started shifting their focus towards the security of their connected electronic programmable products recently since cars used to be mainly mechanical products. However, this has changed due to the rising digitalization of vehicles. Security and functional safety have grown together and need to be addressed as a single issue, referred to as automotive security, in the following article. One way to accomplish security is automotive security education. The scientific contribution of this paper is to establish an Automotive Penetration Testing Education Platform (APTEP). It consists of three layers representing different attack points of a vehicle. The layers are the outer, inner, and core layers. Each of those contains multiple interfaces, such as Wireless Local Area Network (WLAN) or electric vehicle charging interfaces in the outer layer, message bus systems in the inner layer, and debug or diagnostic interfaces in the core layer. One implementation of APTEP is in a hardware case and as a virtual platform, referred to as the Automotive Network Security Case (ANSKo). The hardware case contains emulated control units and different communication protocols. The virtual platform uses Docker containers to provide a similar experience over the internet. Both offer two kinds of challenges. The first introduces users to a specific interface, while the second combines multiple interfaces, to a complex and realistic challenge. This concept is based on modern didactic theory, such as constructivism and problem-based learning. Computer Science students from the Ostbayerische Technische Hochschule (OTH)Regensburg experienced the challenges as part of a special topic course and provided positive feedback.