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The use of quantum processing units (QPUs) promises speed-ups for solving computational problems, in particular for discrete optimisation. While a few groundbreaking algorithmic approaches are known that can provably outperform classical computers, we observe a scarcity of programming abstractions for constructing efficient quantum algorithms. A good fraction of the literature that addresses solving concrete problems related to database management concentrates on casting them as quadratic unconstrained binary optimisation problems (QUBOs), which can then, among others, be processed on gate-based machines (using the quantum approximate optimisation algorithm), or quantum annealers. A critical aspect that affects efficiency and scalability of either of these approaches is how classical data are loaded into qubits, respectively how problems are encoded into QUBO representation. The effectiveness of encodings is known to be of crucial importance for quantum computers, especially since the amount of available qubits is strongly limited in the era of noisy, intermediate-size quantum computers.
In this paper, we present three encoding patterns, discuss their impact on scalability, and their ease of use. We consider the recreational (yet computationally challenging) Sudoku problem and its reduction to graph colouring as an illustrative example to discuss their individual benefits and disadvantages. Our aim is enable database researchers to choose an appropriate encoding scheme for their purpose without having to acquire in-depth knowledge on quantum peculiarities, thus easing the path towards applying quantum acceleration on data management systems.
Abstraction layers are of paramount importance in software architecture, as they shield the higher-level formulation of payload computations from lower-level details. Since quantum computing (QC) introduces many such details that are often unaccustomed to computer scientists, an obvious desideratum is to devise appropriate abstraction layers for QC. For discrete optimisation, one such abstraction is to cast problems in quadratic unconstrained binary optimisation (QUBO) form, which is amenable to a variety of quantum approaches. However, different mathematically equivalent forms can lead to different behaviour on quantum hardware, ranging from ease of mapping onto qubits to performance scalability. In this work, we show how using higher-order problem formulations (that provide better expressivity in modelling optimisation tasks than plain QUBO formulations) and their automatic transformation into QUBO form can be used to leverage such differences to prioritise between different desired non-functional properties for quantum optimisation. Based on a practically relevant use-case and a graph-theoretic analysis, we evaluate how different transformation approaches influence widely used quantum performance metrics (circuit depth, gates count, gate distribution, qubit scaling), and also consider the classical computational efforts required to perform the transformations, as they influence possibilities for achieving future quantum advantage. Furthermore, we establish more general properties and invariants of the transformation methods. Our quantitative study shows that the approach allows us to satisfy different trade-offs, and suggests various possibilities for the future construction of general-purpose abstractions and automatic generation of useful quantum circuits from high-level problem descriptions.
Quantum software is becoming a key enabler for applying quantum computing to industrial use cases. This poses challenges to quantum software engineering in providing efficient and effective means to develop such software. Eventually, this must be reliably achieved in time, on budget, and in quality, using sound and well-principled engineering approaches. Given that quantum computers are based on fundamentally different principles than classical machines, this raises the question if, how, and to what extent established techniques for systematically engineering software need to be adapted. In this chapter, we analyze three paradigmatic application scenarios for quantum software engineering from an industrial perspective. The respective use cases center around (1) optimization and quantum cloud services, (2) quantum simulation, and (3) embedded quantum computing. Our aim is to provide a concise overview of the current and future applications of quantum computing in diverse industrial settings. We derive presumed challenges for quantum software engineering and thus provide research directions for this emerging field.
Federated online social networks are an alternative to centralized and often profit-driven social networks. Instead of providing exactly one main platform, federated and decentralized approaches consist of multiple platforms, nodes or instances, leading to new challenges for guaranteeing confidentiality, integrity and availability. In addition, privacy is taken into close consideration due to the sensitive nature of processed personal data and the purpose of online social networks as well as the user behavior on social media. The recent popularity and broad use of the federated micro-blogging platform Mastodon issues the matter of security and privacy challenges for this type of architecture and the specific platform as well. Mastodon is part of a larger network called Fediverse with several platforms with different purposes. Communication and interoperability between Fediverse platforms is mostly achieved by ActivityPub protocol as standard for decentralized social networking, defined by W3C. We analyze Mastodon as the currently most prominent and largest example of a Fediverse platform. Therefore, we perform tests for typical types of software vulnerabilities as well as evaluate common security challenges built into its design. As a result, we identify trust as security principle as critical issue, leading to multiple weak points such as enabling attackers and malicious actors to spread misleading information as well as network availability impacts. We suggest possible solutions customized to our findings as well as general security recommendations when building a federated online social network such as the Fediverse
Distributed Denial of Service attacks are among the most common and widespread network attacks. Due to their nature, they are difficult to defend. Intrusion detection systems, based on machine learning, are a promising approach to counter this threat. But to train these systems, data sets with Distributed Denial of Service attacks are needed. An implemented Python program, which creates Denial of Services packets and simulates distributed sending by multithreading, is presented. Unlike synthetically generated data with the use of simulators, real network traffic is generated. This eliminates errors and offers a better basis of data, as machine learning algorithms need data that is as error-free as possible in order to learn efficiently.
Besides application fields such as entertainment and marketing, the technology of Virtual Reality is also applied in research and practice, including domains such as automotive, architecture, and construction. Furthermore, VR has been utilized for several activities in design practice and design research. However, the impact of this technology on design as a profession remains to be investigated. Thus, in the present study, an integrative literature review is presented to investigate the potential impact of Virtual Reality on design research and design practice. The findings indicate potential advantages on different levels. VR has the potential to enhance problem identification due to laboratorial environments and to foster co-creation due to enhanced motivation and the establishment of synergies between the involved stakeholders. Furthermore, it supports prototyping activities due to its capabilities for realistic scaling and perspectives. And lastly, VR has proven to be advantageous for design evaluations and reviews due to its visualization and immersion potential. Drawbacks of using VR for design research and practice involve technical limitations such as restricted field of view, limited performance, but also aspects such as missing accuracy for prototyping and the absence of haptic feedback. Future work will involve an extended review involving further literature and application domains.
Batteries are used in numerous applications such as mobile devices, electric vehicles, home storage systems and islanded microgrids. Bidirectional DC-DC converters are vital for the integration of batteries, for the power conversion during (dis)charge and the battery management. Modeling of these is helpful, especially for the design of larger, more complex systems consisting of multiple DC-DC converters in parallel. Due to the high switching frequencies, the simulation of DC-DC converters is associated with increased computational time and effort. In this paper, three models of different complexity and accuracy are proposed for a bidirectional DC-DC converter consisting of two phase-shifted half-bridges. Two switching models, which differ mainly in the way the mosfets are driven, account for the individual switching operations and exhibit high accuracy. An averaging model replaces the switching elements with current and voltage sources providing the mean values. It is particularly suitable for multiple components and longer simulation durations. The dynamic behavior of the models is analyzed using the step responses of the load current. For validation, these are compared with the theoretical transfer function. The three models are analyzed comparatively in terms of computational time and effort. The calculation time of the averaging model has been reduced by two thirds compared to the strictly complementary switching model and by 96% relative to the model with diode emulation mode. The averaging model requires only one third of the computation time of the complementary switching model and only 3.5% of that of the model with diode emulation. Recommendations for the use of the models are given and a possible use case is shown. Two parallel connected DC-DC converters with load current sharing between them are simulated using the averaging model.
In recent years, cybersecurity has also become relevant for Operational Technology (OT). Critical systems like industrial automation systems or transportation systems are faced with new threats, and therefore require the implementation of thorough security measures. Regulations further mandate the deployment and regular verification of these security measures. However, OT systems differ from well-known systems of classic Information Technology (IT), such as mission times spanning decades, infrequent updates only during on-site maintenance, or diverse devices with varying support for security measures. The growing field of crypto-agility examines approaches to integrate security measures in an agile and flexible way, making updates easier and, therefore, encouraging a more frequent deployment of them. This paper contributes to this research field in the context of secure communication in two ways. We first examine the current state of crypto-agility by providing an overview of existing measures for OT systems. Then, we propose a new architecture concept with different deployment approaches to integrate security measures in a crypto-agile way. Based on a security library with a generic interface and a flexible proxy application, our architecture is capable of securing both new OT systems and existing ones via retrofit.
Modern education through Learning Management Systems (LMSs) provides learners with personalized learning paths. This is achieved by first querying the learning style according to the theory of Felder and Silverman to recommend suitable learning content. However, a rigid learning style representation is lacking of adaptability to the learners' choices. Therefore, the present study evaluates the idea of providing adaption to the representation of learning styles by using Hidden Markov Models (HMMs). Thus, data is collected from participants out of the Higher Education Area. The Index of Learning Styles questionnaire is used to obtain the learning style based on the theory of Felder and Silverman. Also, a questionnaire that asks the respondents to create a preferred learning path with the sequence length of nine learning elements is provided. From the given data, we initially evaluate the probability relationships between learning styles and learning elements. Then, we use the Viterbi algorithm in HMMs to identify alterations in learning styles from the provided learning paths. The alignment is then quantified by introducing a metric called support value. The findings imply that our concept can be used to adapt the learning style based on the user's real choice of learning elements. Thus, the proposed model also offers a way to integrate a feedback loop within LMSs leading to an improvement of learning path recommendation algorithms.
Learning management systems gain importance due to the Corona pandemic. To personalize such a learning management system, a Moodle adaption named Pythia was created. Pythia is implemented as a Moodle plugin solution. The possibility of generating learning paths with various algorithms and the change of the graphical user interface is significant. Learning paths are generated with two different algorithms. Nestor algorithm generates learning paths with a Bayesian network, while Tyche algorithm uses a Markov model. The graphical user interface is adapted to the generated learning paths where learning element symbols are customized and the individual learning paths are depicted.
This paper presents the results of a qualitative survey among German students with the aim of evaluating the first version of the learning management system Pythia. 25 students took the subject “Software Engineering for Safe and Secure Systems” in the winter term of 2023/24. The 68 asked questions focus on usage behavior, graphical user interface, usability, preferences, volume, and quality of learning elements, and learning paths. Our Moodle course for the lecture contains seven sections, each section has minimum one subsection on which the learning path is calculated. Last three sections are designed by presenting minimum one learning element of each learning element category except the collaboration tool. In a previous work, ten learning element categories were designed and evaluated comprising manuscript, brief overview, learning goal, quiz, exercise, summary, three types of additional material, and collaboration tool. The first sections are left as they are initially designed by the lecturer (normal section). The survey considers the difference between normal sections and diverse sections. Furthermore, the generated learning paths are evaluated. 13 students undertook Nestor learning paths, whereas 12 undertook Tyche learning paths.
The results suggest that for the majority of students, the learning element symbols are well-chosen. Moreover, all students wish the elements relevant to the exam to be labelled. 23 students are overwhelmed by the number of learning elements in the last sections of the Moodle course. In the future, some suggestions will be implemented such as the labelling of exam relevant learning elements. To overcome the overload of learning elements, a strategy could be developed like hiding learning elements not preferred by the learning path algorithm.
During the COVID-19 pandemic, the importance of digital course rooms, where teachers provide their learning materials, increased dramatically. While these platforms are crucial for providing teaching materials, they often fall short in addressing individual student needs. A system within an academic setting, capable of creating and presenting individual learning paths for each student, can solve these issues. These paths are composed of various learning elements - defined in our previous work as units of educational content with which a learner works.
Currently, there is no suitable system that enables the integration of learning path generating algorithms into a digital course room. Therefore we present an application that enables this integration into the Moodle Learning Management System (LMS). More precisely, this paper presents a Moodle plugin together with its framework. It describes the mechanism for effectively collecting data from Moodle, which AI algorithms then use to generate personalized learning paths. Subsequently these paths are visualized with the help of the Moodle plugin.
We started with a set of requirements and use cases for the interface connecting Moodle to the AI system, which were established with a group of experts. Based on the requirements, various relevant technologies were assessed, and the best ones were chosen for implementation.
Following that, the paper develops a strategy for software structuring as well as an architecture, focusing on performance, modularity, and ease of deployment for widespread use. Furthermore, the architecture ensures a simple method for integrating the algorithms. Afterwards, the framework's concrete implementation is described. A technique for enriching learning elements with metadata is presented, and additionally a concept for presenting these learning elements within a hierarchy. Moreover, it is shown how questionnaire responses and learning analytics are utilized for data collection. We cover in detail techniques for extracting and storing data from the Moodle database, as well as methods for customizing Moodle course rooms and a standard API for incorporating AI algorithms.
Finally, the paper discusses the application of the proposed framework in an actual course and how student feedback is collected, which could enhance the framework. It concludes with an assessment of the outcomes obtained and prospects for the framework's future advancements.
EYE TRACKING AS TECHNOLOGY IN EDUCATION: FURTHER INVESTIGATION OF DATA QUALITY AND IMPROVEMENTS
(2024)
Eye tracking serves as a powerful tool across a variety of empirical research areas: From usability research over cognitive research to educational research and applications in classrooms. However, data noise in eye tracking data poses a challenge to researchers and educators, as it leads to gaze positions being measured imprecisely under unfavorable conditions.
In our previous study, we systematically investigated factors that influence data quality and are easily controllable in a classroom or laboratory environment, such as illumination, sampling frequency, and head orientation. However, no recommendations regarding the light source and light orientation could be provided, as these influences could not be analyzed in sufficient detail. Yet, a further examination of these factors, eliminating human influences by using an artificial head, revealed significant differences between individual settings.
Hence, in this empirical study of eye tracking as an educational technology, we delve deeper into examining the impact of both light source and light orientation on data quality. This is investigated with an artificial head together with the Tobii Pro Spectrum eye tracking device.
To measure data quality, we use the metrics precision and standard deviation as indicators of data noise. The obtained results derive practical advice for educators and researchers, such as not to illuminate the subject from the rear, in order to gather useful data for research and future classroom applications.
Thereby, this study serves as a complement to our previous research, answering open questions regarding best practices for researchers and educators when using eye trackers. It aims to provide valuable insights into producing data of the highest quality possible when using eye trackers, both in laboratory settings and in future classrooms applications.
Global and focal eye tracking gaze patterns are distinguished in a variety of domains, such as radiology research, empirical software engineering, behavioral psychology, and cartography research. A global gaze pattern is present if a participant in an eye tracking study gains an overview of the stimulus, whereas a focal pattern emerges during a more detailed evaluation of a specific part of the stimulus.
With this higher-level measure of global and focal gaze patterns, cognitive effort, expertise levels, and even neurological characteristics - such as autism - can be quantified. Furthermore, with this measure, cognitive efforts of students in certain tasks can be investigated and clues for targeted support can be provided. These properties in particular make this eye tracking measure valuable for the education domain.
In the field of eye tracking research, many researchers have established distinct criteria for differentiating global and focal gaze patterns. Moreover, it is rarely quantitatively measured which of these patterns is present, but rather determined based on qualitative considerations. However, some studies explicitly use quantitative metrics like saccade-length or fixation-duration to distinguish between global and focal gaze patterns. This is the focus of the present systematic literature review: It searches for and compiles the eye tracking metrics used in the literature to distinguish global from focal gaze patterns in a quantitative way.
In doing so, this study fills the yawning gap in quantitative metrics for determining global and focal gaze patterns. In the long run, this can be used to answer questions about cognitive load and problems in processing tasks during eye tracking studies and to provide further insights into the cognitive processes of students.
The present paper first discusses definitions of global and focal gaze patterns and presents examples in which this metric has already been applied. Then, the research questions together with search strings and search engines used for the systematic literature review are described. Finally, the results are summarized, presenting a compilation of and connections between quantitative metrics utilized in the literature to distinguish between global and focal viewing patterns.
In the intricate tapestry of the cosmos, where celestial threads weave stories of destiny, an enigmatic deity stands at the intersection of chance and fate — Tyche, the goddess of fortune. In science and probability, coincidence plays a distinctive role in Bayesian Networks (BNs) and Markov Models (MMs). This paper introduces the Tyche algorithm named after the goddess of fortune. The Tyche algorithm is a Markov model designed to generate learning paths in Learning Management Systems (LMSs). A learning path is a type of individualization that personalises the order of learning elements within an LMS course. Learning elements are fundamental components within an LMS course, depicting the learning content in diverse ways. In our case, the learning path tailoring is based on the learner’s learning style according to Felder-Silverman Learning Style Model (FSLSM) – an indicator for the ideal pathway and learning element for the learner’s optimum learning.
The Tyche algorithm offers the advantage to provide students the most suitable learning path. Tyche is a MM structure with various matrices containing transition probabilities depending on the learning style. Nine categories of learning elements defined in a previous survey form the basis of the node structure of the MM. For a generic approach, a survey was designed to obtain the transition probabilities depending on the individual learning style. The survey with more than 100 German students participated is processed with the tool LimeSurvey. Students are asked about their learning style using the Index of Learning Styles (ILS) questionnaire according to Felder-Silverman and about the percentage probabilities of learning elements to get their individual sequence as learning path. The percentages are queried in two different ways. Firstly, the students were asked to provide a sequence of learning elements within each position of the sequence filled with probabilities for all learning elements. Secondly, a learning element is given and the students are asked to indicate which learning element they would work on next. The first way of asking for the probabilities is used to find the start node in the MM, whereas the second approach forms the probability matrices between the nodes within the MM.
As result of the survey, the Tyche algorithm presents generic transition probabilities. It improves the learning process of individuals only by asking for their learning style: it generates individual learning paths through the learning elements within an LMS based on the MM explained above by solely getting the answers of the ILS questionnaire as input. In the future, other questionnaires such as BFI-10 for personality traits or LIST-K for learning strategies may offer a more comprehensive input. However, the next step is to evaluate Tyche with about 25 students in a software engineering lecture. This is planned for the year 2024.
The paper “Ready to Leap (by Co-Design)? Join Order Optimisation on Quantum Hardware” proposes the first approach to solve the problem of join order optimization on quantum hardware. The authors characterize the applicability and limitations of current state-of-the-art quantum hardware, i. e. gate-based quantum computing and quantum annealing, for join ordering and recommend key improvements to the physical hardware to reach practical utility. Based on the provided database queries and QPU system processing data, we have been able to reproduce the original paper’s key insights and quantum problem characteristics reported in its experimental section. The authors provided a self-contained and fully automated reproduction package, including data (database queries, statistics, and collected QPU processing data), experiment scripts, and plotting routines that
allowed the identical reconstruction of the three main figures in the paper.
Weather radars are highly sophisticated tools for quantitative precipitation estimation (QPE) and provide observations with unmatched spatial representativeness. However, their indirect measurement of precipitation high above ground leads to strong systematic errors compared to direct rain gauge measurements. Additionally, the temporal undersampling from 5-min instantaneous radar measurements requires advection correction. We present ResRadNet, a 3-D-convolutional residual neural network approach, to reduce these errors and, at the same time, increase the temporal resolution of the radar rainfall fields by a 5-min short-range prediction of 1-min time-steps. The network is trained to process spatiotemporal sequences of radar rainfall estimates from a composite product derived from 17 C-band weather radars in Germany. In contrast to previous approaches, we present a method that emphasizes the generation of spatiotemporally consistent and advection-corrected country-wide rainfall maps. Our approach significantly increased the Pearson correlation coefficient (PCC) of the radar product (from 0.63 to 0.74) and decreased the root mean squared error (mse) by 22% when compared to 247 rain gauges at a 5-min resolution. An additional large-scale comparison to eight years of data from 1138 independent manual daily gauges confirmed that the improvement is robust and transferable to new locations. Overall, our study shows the benefits of using 3-D convolutional neural networks (CNNs) for weather radar rainfall estimation to provide 1-min, ground-adjusted, that is, bias-corrected with respect to on-ground sensors, and advection-corrected radar rainfall estimates.
Eye tracking is becoming more and more important as a research method within the field of software engineering (SE). Existing meta-analyses focus on the design or conduct of SE eye tracking studies rather than the analysis phase. This article attempts to fill this gap; it presents a systematic literature review of eye tracking studies in the field of SE—focusing mainly on the data analysis methods used. From the IEEE Xplore and ACM digital libraries we gather 125 papers up to the first quarter of 2024. Detailed evaluation provides information on the number of papers that use specific methods of analysis (i.e., descriptive or inferential statistics, and gaze visualization) or settings (e.g., sample size, technical setup, and selected aspects of research design). With the data obtained we can infer the popularity of specific analysis methods in the field. Those results enable efficient work on data analysis tools or education of aspiring researchers and can serve as basis for standardization or guidelines within the community—providing for methods to include as well as current inconsistencies.
Assessing Heuristic Evaluation in Immersive Virtual Reality: A Case Study on Future Guidance Systems
(2023)
A variety of evaluation methods for user interfaces (UI) exist such as usability testing, cognitive walkthrough, and heuristic evaluation. However, UIs such as guidance systems at transit hubs must be evaluated in their intended application field to allow the effective and valid identification of usability flaws. However, what if evaluations are not feasible in real environments, or laboratorial conditions cannot be ensured? Based on adapted heuristics, in the present study, the method of heuristic evaluation is combined with immersive Virtual Reality (VR) for the identification of usability flaws of dynamic guidance systems (DGS) at transit hubs. The study involved usability evaluations of nine DGS concepts using the newly proposed method. The results show that compared to computer-based heuristic evaluations, the use of immersive VR led to the identification of an increased amount of “severe” usability flaws as well as overall usability flaws. Within a qualitative assessment, immersive VR is validated as a suitable tool for conducting heuristic evaluations involving significant advantages such as the creation of realistic experiences in laboratorial conditions. Future work seeks to further prove the suitability of using immersive VR for heuristic evaluations and compare the proposed method to other evaluative methods.
Quantum computing is an interdisciplinary field that relies on the expertise of many
different stakeholders. The views of various stakeholders on the subject of quantum computing may differ, thereby complicating communication. To address this, we propose a view-based quantum development approach based on a Single Underlying Model (SUM) and a supporting quantumIntegrated Development Environment (IDE). We highlight emerging challenges for future research.
The quantum approximate optimisation algorithm (QAOA) is at the core of many scenarios that aim to combine the power of quantum computers and classical high-performance computing appliances for combinatorial optimisation. Several obstacles challenge concrete benefits now and in the foreseeable future: Imperfections quickly degrade algorithmic performance below practical utility; overheads arising from alternating between classical and quantum primitives can counter any advantage; and the choice of parameters or algorithmic variant can substantially influence runtime and result quality. Selecting the optimal combination is a non-trivial issue, as it not only depends on user requirements, but also on details of the hardware and software stack. Appropriate automation can lift the burden of choosing optimal combinations for end-users: They should not be required to understand technicalities like differences between QAOA variants, required number of QAOA layers, or necessary measurement samples. Yet, they should receive best-possible satisfaction of their non-functional requirements, be it performance or other. We determine factors that affect solution quality and temporal behaviour of four QAOA variants using comprehensive density-matrix-based simulations targeting three widely studied optimisation problems. Our simulations consider ideal quantum computation, and a continuum of scenarios troubled by realistic imperfections. Our quantitative results, accompanied by a comprehensive reproduction package, show strong differences between QAOA variants that can be pinpointed to narrow and specific effects. We identify influential co-variables and relevant non-functional quality goals that, we argue, mark the relevant ingredients for designing appropriate software engineering abstraction mechanisms and automated tool-chains for devising quantum solutions from high-level problem specifications.
As implementations of quantum computers grow in size and maturity, the question of how to program this new class of machines is attracting increasing attention in the software engineering domain. Yet, many questions from how to design expressible quantum languages augmented with formal semantics via implementing appropriate optimizing compilers to abstracting details of machine properties in software systems remain challenging. Performing research at this intersection of quantum computing and software engineering requires sufficient knowledge of the physical processes underlying quantum computations, and how to model these. In this chapter, we review a superoperator-based approach to quantum dynamics, as it can provide means that are sufficiently abstract, yet concrete enough to be useful in quantum software and systems engineering, and outline how it is used in several important applications in the field.
Alle Akteure in der Hochschullandschaft auf die Herausforderungen und Chancen der KI vorzubereiten und zu begleiten ist ein wesentlicher Beitrag für zukünftiges Lehren, Lernen und Forschen. Die Autoren der Hochschule Magdeburg-Stendal zeigen in diesem Artikel auf, wie KI- Kompetenzen in ingenieurswissenschaftlichen Studiengängen durch den Einsatz eines Lerntagebuches entwickelt und gefördert werden können. Durch das Modul „Einführung in die KI – Grundlagen und Anwendungsfelder“ ist ein spezielles Kursdesign geschaffen worden, welches als wertvolles Lehr-Lernangebot der Studentinnen und Studenten evaluiert wurde.
Neue Herangehensweisen im Innovationsprozess werden für Unternehmen immer wichtiger, um konkurrenzfähig zu bleiben. So bezeichnet Intrapreneurship das Vorgehen, Angestellte innerhalb von Unternehmen selbst als Unternehmerinnen und Unternehmer agieren zu lassen, um so Innovation zu fördern. Der Begriff Design Thinking beschreibt ein Vorgehen, mit Hilfe dessen uneindeutig definierte Probleme behandelt und nutzerzentrierte Produkte entwickelt werden können. Die Prämisse dieses Beitrags ist es, dass die Kombination von neuartigen Technologien und besonders Virtual Reality mit Design Thinking den Innovationsprozess innerhalb von Unternehmen fördern kann. Anhand zweier Fallbeispiele wird dargestellt, dass neben Nachteilen technischer Natur besonders die Möglichkeit fiktive Szenarien in VR darzustellen und valide Nutzerdaten in laborähnlichen Verhältnissen zu sammeln, Innovation in Unternehmen anregen kann. In weiteren Schritten soll die Nutzung von Design Thinking und Virtual Reality in neuen Applikationsdomänen sowie die Nutzung weiterer Designmethoden untersucht werden.
Embracing virtual reality
(2024)
This chapter investigates the potential impacts of virtual reality (VR) technology on the design process, particularly in engineering and design fields. VR, defined as a computer-generated simulation, has already found applications in various professional domains such as engineering, training, marketing, and ergonomics. However, its potential influence on the design process remains to be explored. The aim of this investigation is to assess the impacts of VR on professionals, users, technology, and the design profession as a whole. The chapter proposes a set of general guidelines for incorporating VR in the design process and emphasizes the need for human-centered approaches to enhance the acceptability and acceptance of VR. While VR offers tangible advantages and benefits, it also presents limitations and challenges that must be addressed for it to be established as a strategic design tool. The chapter concludes by suggesting the development of a taxonomy for evaluating and utilizing VR and emphasizes the importance of considering human-centered approaches and integrating artificial intelligence tools to enhance design activities further. VR has the potential to foster divergent and convergent thinking, facilitate co-creative activities, improve prototyping and concept evaluation, and enhance immersive product presentations, thereby transforming the roles of designers and the design profession as a whole.
In the design of urban public spaces, the inclusion of diverse voices enhances the development of products and services by synchronising designer expertise with people’s preferences. Multiple participatory methods exist, each with their respective benefits and drawbacks in terms of the quality of results, time and cost needed for preparing and conducting studies, and knowledge required for participation. Providing more concrete representations of abstract or intangible design concepts would be beneficial for laypeople unfamiliar with design or the case study. We propose a Virtual Reality (VR) platform to discover subjective preferences on public waiting rooms through immersive design experiences. The VR platform was tested with 463 participants with variety in age and cultural background. Following a qualitative data analysis, we discuss the suitability of our VR platform for fostering inclusive participation and how it impacts the role of the designer, as well as propose design guidelines for future VR studies.
Die Automatisierung macht auch vor den öffentlichen Verkehrsmitteln nicht halt. Das Team Design for Autonomous Mobility von TUMCREATE stellt Ergebnisse aus der Designforschung vor, in der Virtual Reality und andere neue Methoden für das Design autonomer Verkehrsmittel untersucht werden. Das Team orientiert sich dabei stark am Menschen und versucht, allen Passagieren mehr Komfort und ein positives Reiseerlebnis zu bieten.
Towards user acceptance of autonomous vehicles: a virtual reality study on human-machine interfaces
(2020)
Technological advances in businesses related to automated transportation raise challenges. Indeed, ensuring safety for users is essential for the future commercial launch of this technology. Within this study, human-machine interfaces (HMIs) are evaluated regarding the communication between autonomous vehicles (AVs) and pedestrians in order to prepare a successful deployment of the technology on the market. Since real-life experiments involving AVs remain dangerous, experiments were conducted via virtual reality (VR). The results show that beyond the need for HMIs, display-based concepts were more usable than laser projections. The study impacts on: 1) substantiating the need for explicit HMIs on AVs (including concept recommendations) for a successful market entry; 2) proving that VR constitutes an advantageous alternative for conducting experiments in the field of autonomous transportation for both research and business. Further work is needed that involves more participants and an improved virtual environment.
Computer-Aided Design (CAD) constitutes an important tool for industrial designers. Similarly, Virtual Reality (VR) has the capability to revolutionize how designers work with its increased sense of scale and perspective. However, existing VR CAD applications are limited in terms of functionality and intuitive control. Based on a comparison of VR CAD applications, ImPro, a new application for immersive prototyping for industrial designers was developed. The user evaluations and comparisons show that ImPro offers increased usability, functionality, and suitability for industrial designers.
This chapter introduces SuperMod, an approach and tool to support the evolution of software product lines (SPLs) by means of a filtered editing model, which is inspired by the checkout-modify-commit workflow established in version control systems. Rather than forcing the developers into editing multi-variant artifacts of an SPL, SuperMod allows them to perform modifications successively in single-variant workspaces and to integrate the changes by indicating the logical scope, i.e., the affected variants, of the change performed. The SPL itself is managed automatically in a transparent repository by the system. As a consequence, developers may re-use the same engineering tools they also used for the development of the product variants prior to the re-engineering process. SuperMod furthermore orchestrates collaborative development and provides dedicated support for (without restricting developers to) model-driven approaches to SPL engineering. We illustrate SuperMod’s capabilities by the well-known graph SPL example and discuss the practical benefits of the suggested solution to SPL evolution as well as its relation to SPL re-engineering.
Abstract
Motivation
Recognition of specific molecules by proteins is a fundamental cellular mechanism and relevant for many applications. Being able to modify binding is a key interest and can be achieved by repurposing established interaction motifs. We were specifically interested in a methodology for the design of peptide binding modules. By leveraging interaction data from known protein structures, we plan to accelerate the design of novel protein or peptide binders.
Results
We developed ATLIGATOR—a computational method to support the analysis and design of a protein’s interaction with a single side chain. Our program enables the building of interaction atlases based on structures from the PDB. From these atlases pocket definitions are extracted that can be searched for frequent interactions. These searches can reveal similarities in unrelated proteins as we show here for one example. Such frequent interactions can then be grafted onto a new protein scaffold as a starting point of the design process. The ATLIGATOR tool is made accessible through a python API as well as a CLI with python scripts.
Availability and implementation
Source code can be downloaded at github (https://www.github.com/Hoecker-Lab/atligator), installed from PyPI (‘atligator’) and is implemented in Python 3.
Orthogonal Procrustes Based Anomaly Detection and Error Prediction for Vehicle Bills of Materials
(2022)
Industrial Bill of Materials (BOM) suffer from an surging complexity and cause errors in production which have detrimental effects on a product’s profitability. Currently, BOM anomalies have to be identified manually and errors have to be detected in the same way. This preprint describes a combination of data analysis and Machine Learning methods, such as hierarchical and agglomerative clustering, an isolation forest algorithm, association mining and a multi-output Artificial Neural Network, all based on a deterministic distance measure for an industrial BOMs. Solving the orthogonal Procrustes problem for complex, multi-level matrices, a distance measure for real world industrial BOMs was derived. A multi-output MLP was used in order to predict error probabilities with a time- reference. Our results show how to detect anomalies and predict errors in a complex, multi-level BOM based on historical, labelled data. While other authors focus on the mere comparison of BOMs, we aimed at a holistic approach, combining descriptive and predictive methods in order to forecast where in a BOM and at what time of BOM creation process errors occur. The resulting, prescriptive system was tested using real world data and has shown to effectively predict where and when BOM errors are probable to occur. Consequently, the prescriptive system is superior to prior, purely predictive systems, can help to decrease errors and thereby decreases product development time and cost in real world companies.
Version control systems are an integral part of today’s software engineering. They facilitate the collaborative management of revisions (sequential versions) and variants (concurrent versions) of software systems under development. Typical version control systems maintain revisions of files and variants of whole software systems. Variants are supported via branching or forking mechanisms that conceptually clone whole systems in a coarse-grained way. Unfortunately, such cloning leads to high maintenance efforts. To avoid these disadvantages and support fine-grained variation, developers need to employ custom configuration mechanisms, which leads to a misappropriation of tools and undesired context switches. Addressing this trade-off, a number of variation control systems has been conceived, providing a richer set of capabilities for handling variants. Variation control systems decompose a software system into finer-grained variable entities and offer high-level metaphors to automatically manage this variability. In this paper, we classify and compare variation control systems and illustrate their core concepts and characteristics. All investigated variation control systems offer an iterative (checkout–modify–commit) workflow, but there are essential differences affecting developers. We highlight challenges and discuss research perspectives for developing the next generation of version and variation control systems.
Enhancing Expert Interviews: Insights from Information Systems and Digital Transformation Research
(2024)
Expert interviews, a commonly employed qualitative data collection procedure in information systems (IS) research, lack consistent conceptualization. This paper aims to address this gap by providing a conceptual framework and comprehensive guidelines for a rigorous implementation of expert interviews, supported by real-world examples. After a systematic selection of method-relevant literature, a thematic analysis of twenty-eight articles, books, and book chapters is conducted to elicit the distinctive characteristics and rigorous conduct of expert interviews. Validation is provided by analyzing nineteen studies published in important IS outlets that use expert interviews. A particular focus is on a subset of five studies that cover digital transformation topics. The analysis reveals expert interviews’ flexibility as data collection procedure and shows the wide range of application opportunities for IS researchers. Lastly, we discuss theoretical and practical implications of our findings to enhance the rigor, systematic use, and relevance of expert interviews in IS research.
Digital representations of buildings are the supporting structures of various use-cases in the emerging field of data-driven decision making. From large scale applications in the context of city planning to the detailed evaluation of critical infrastructure they enable specialists to observe problems, interpret relationships, test solutions virtually and apply them in the real world. This is only feasible if the individual underlying digital model meets the requirements imposed by the analysis at hand.
In practice, especially models of existing buildings are not easy to come by as the information describing the existing structure is often scattered across multiple different data sources in various formats. Previous research efforts have outlined methodologies which leverage machine learning, computer vision and subsequent semantic enrichment in order to achieve the (re)construction of such building models. However, these methods are generally not integrated with each other, nor do they consider being able to interface with a shared repository of building related data.
In this paper we present a methodology which focuses on establishing a common context for all building related data by utilizing the Industry Foundation Classes (IFC) schema. In particular we focus on utilizing readily available geometric and semantic data originating from geographic information systems as a basis, subsequently referencing additional data sources in their corresponding context and finally outlining interfaces with downstream enrichment processes in both directions. Through incorporating contextualized (IFC) data into the early stages of the remodeling workflow, we outline an end-to-end process from the initial component-based data-acquisition to the as-built building information model. In establishing a standardized foundation for data exchange and collaboration it enables all stakeholders to work more seamlessly across different stages of the remodeling project.
Enhancing Decision-Making for Human-Centered Construction Robotics: A Methodological Framework
(2024)
While the Architecture, Engineering, and Construction (AEC) industry is increasingly aware of the rising demands for productivity and human-centered construction improvements, the holistic adoption of robotics as a fundamental strategy to address these challenges has not yet reached comprehensive fruition. This paper therefore introduces a methodological framework aiming to address the industry's pressing need for a systematic approach for assessing the feasibility of integrating robotics into human-centered construction processes.
It aims to enhance decision-making regarding the degree of automation in human-centered construction processes, ranging from partial to full robotization or non-robotization. The framework is characterized by a more holistic end-to-end data-/workflow and therefore adopts a multifaceted approach, leveraging BIM-based planning methodologies and integrating new technologies [e.g., Motion Capturing (MoCap), work process simulation software incorporating Digital Human Models (DHM), self-developed conversion/interfacing software and more] that have not been widely used in the industry to date.
Subsequently, the framework is evaluated in a real-life bricklaying construction process to ensure a more application-based approach. Overall, the framework advances current construction processes with a more inclusive and conscious technology infill to empower construction professionals with the workflow and corresponding tools necessary for the practical integration of robotics into human-centered construction processes.
For manufacturing firms, success in innovating IT-enabled services is a critical antecedent to benefit from digital servitization of their business models. Digital servitization literature has explored mechanisms for success in innovating IT-enabled services, indicating that the phenomenon is multifaceted and needs to be explained from multiple theoretical perspectives. We derive a conceptual model for success in innovating IT-enabled services covering its multifaceted nature by referring to knowledge-based and organizational control theory. We test this model using qualitative cases of IT-enabled service innovation initiatives in manufacturing firms and use set-theoretic analyses to account for the multifaceted nature of the phenomenon. The necessary condition analysis yields that a certain degree of service innovation capabilities is a prerequisite for success. With the results of a qualitative comparative analysis, we obtain five solution terms as causal recipes for success in innovating IT-enabled services. Our results contribute to research by offering a theory-based approach that explains the multiplicity of success in IT-enabled service innovation. Practitioners benefit from our results by understanding prerequisites and causal recipes for success while learning from unsuccessful initiatives in innovating IT-enabled services of manufacturing firms. Our study is also an example of how to rigorously calibrate qualitative data using a structured approach.
In higher education, improving learning and learning success are goals of general improvement. Lecturers teaches content and students acquire that content in an efficient way. To structure content, learning element categories are evaluated from the student's point of view in higher education area. The aim is to validate given definitions of ten learning element categories within a Learning Management System (LMS).
This paper evaluates a categorization of learning elements for organizing learning content in online education within LMSs. Therefore, ten categories of learning elements and corresponding definitions were defined in a previous work as base for this paper. The learning elements to examine are manuscript, exercise, quiz, brief overview, learning goal, summary, collaboration tool, auditory additional material, textual additional material, and visual additional material. To validate the definitions and to get improvements to each learning element a survey is processed. Beside the demographic data questions, the survey consists of two questions to the acceptance of the definitions and asks for improvements. 148 students between the ages 19 and 35 participate in the survey in summer term 2023. The education level of the participants ranges from undergraduates to Ph.D. students.
The results of this paper are that more than 80% accept the given definitions. Some definitions of the learning elements are changed, but the changes are restricted to additions of maximal four words. This categorization of learning elements could lead to improvements in learning by giving the content more structure. With the structure students get the possibility to learn with preferred learning elements which could lead to more success in learning and to a decreasing dropout rate in universities. In the future, the learning elements allow to classify content within LMSs with the goal of generating individual learning paths. Furthermore, our project will integrate these learning elements, use them to generate learning paths, and could set a new standard in the way of personalized learning.
Interpretable Machine Learning for Mode Choice Modeling on Tracking-Based Revealed Preference Data
(2024)
Mode choice modeling is imperative for predicting and understanding travel behavior. For this purpose, machine learning (ML) models have increasingly been applied to stated preference and traditional self-recorded revealed preference data with promising results, particularly for extreme gradient boosting (XGBoost) and random forest (RF) models. Because of the rise in the use of tracking-based smartphone applications for recording travel behavior, we address the important and unprecedented task of testing these ML models for mode choice modeling on such data. Furthermore, as ML approaches are still criticized for leading to results that are hard to understand, we consider it essential to provide an in-depth interpretability analysis of the best-performing model. Our results show that the XGBoost and RF models far outperform a conventional multinomial logit model, both overall and for each mode. The interpretability analysis using the Shapley additive explanations approach reveals that the XGBoost model can be explained well at the overall and mode level. In addition, we demonstrate how to analyze individual predictions. Lastly, a sensitivity analysis gives insight into the relative importance of different data sources, sample size, and user involvement. We conclude that the XGBoost model performs best, while also being explainable. Insights generated by such models can be used, for instance, to predict mode choice decisions for arbitrary origin–destination pairs to see which impacts infrastructural changes would have on the mode share.
In a number of tomographic applications, data cannot be fully acquired, resulting in severely underdetermined image reconstruction. Conventional methods in such cases lead to reconstructions with significant artifacts. To overcome these artifacts, regularization methods are applied that incorporate additional information. An important example is TV reconstruction, which is known to be efficient in compensating for missing data and reducing reconstruction artifacts. On the other hand, tomographic data are also contaminated by noise, which poses an additional challenge. The use of a single regularizer must therefore account for both the missing data and the noise. A particular regularizer may not be ideal for both tasks. For example, the TV regularizer is a poor choice for noise reduction over multiple scales, in which case ℓ1 curvelet regularization methods are well suited. To address this issue, in this paper, we present a novel variational regularization framework that combines the advantages of different regularizers. The basic idea of our framework is to perform reconstruction in two stages. The first stage is mainly aimed at accurate reconstruction in the presence of noise, and the second stage is aimed 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 illustrate the advantages of our approach in numerical experiments.
Background and objectiveDue to the high prevalence of dental caries, fixed dental restorations are regularly required to restore compromised teeth or replace missing teeth while retaining function and aesthetic appearance. The fabrication of dental restorations, however, remains challenging due to the complexity of the human masticatory system as well as the unique morphology of each individual dentition. Adaptation and reworking are frequently required during the insertion of fixed dental prostheses (FDPs), which increase cost and treatment time. This article proposes a data-driven approach for the partial reconstruction of occlusal surfaces based on a data set that comprises 92 3D mesh files of full dental crown restorations.MethodsA Generative Adversarial Network (GAN) is considered for the given task in view of its ability to represent extensive data sets in an unsupervised manner with a wide variety of applications. Having demonstrated good capabilities in terms of image quality and training stability, StyleGAN-2 has been chosen as the main network for generating the occlusal surfaces. A 2D projection method is proposed in order to generate 2D representations of the provided 3D tooth data set for integration with the StyleGAN architecture. The reconstruction capabilities of the trained network are demonstrated by means of 4 common inlay types using a Bayesian Image Reconstruction method. This involves pre-processing the data in order to extract the necessary information of the tooth preparations required for the used method as well as the modification of the initial reconstruction loss.ResultsThe reconstruction process yields satisfactory visual and quantitative results for all preparations with a root mean square error (RMSE) ranging from 0.02 mm to 0.18 mm. When compared against a clinical procedure for CAD inlay fabrication, the group of dentists preferred the GAN-based restorations for 3 of the total 4 inlay geometries.ConclusionsThis article shows the effectiveness of the StyleGAN architecture with a downstream optimization process for the reconstruction of 4 different inlay geometries. The independence of the reconstruction process and the initial training of the GAN enables the application of the method for arbitrary inlay geometries without time-consuming retraining of the GAN.
With the ongoing miniaturization of wireless devices, the importance of wearable textiles in the antenna segment has increased significantly in recent years. Due to the widespread utilization of wireless body sensor networks for healthcare and ubiquitous applications, the design of wearable antennas offers the possibility of comprehensive monitoring, communication, and energy harvesting and storage. This article reviews a number of properties and benefits to realize comprehensive background information and application ideas for the development of lightweight, compact and low-cost wearable patch antennas. Furthermore, problems and challenges that arise are addressed. Since both electromagnetic and mechanical specifications must be fulfilled, textile and flexible antennas require an appropriate trade-off between materials, antenna topologies, and fabrication methods—depending on the intended application and environmental factors. This overview covers each of the above issues, highlighting research to date while correlating antenna topology, feeding techniques, textile materials, and contacting options for the defined application of wearable planar patch antennas.
Generative deep learning approaches for the design of dental restorations: A narrative review
(2024)
Objectives:
This study aims to explore and discuss recent advancements in tooth reconstruction utilizing deep learning (DL) techniques. A review on new DL methodologies in partial and full tooth reconstruction is conducted.
Data/Sources:
PubMed, Google Scholar, and IEEE Xplore databases were searched for articles from 2003 to 2023.
Study selection:
The review includes 9 articles published from 2018 to 2023. The selected articles showcase novel DL approaches for tooth reconstruction, while those concentrating solely on the application or review of DL methods are excluded. The review shows that data is acquired via intraoral scans or laboratory scans of dental plaster models. Common data representations are depth maps, point clouds, and voxelized point clouds. Reconstructions focus on single teeth, using data from adjacent teeth or the entire jaw. Some articles include antagonist teeth data and features like occlusal grooves and gap distance. Primary network architectures include Generative Adversarial Networks (GANs) and Transformers. Compared to conventional digital methods, DL-based tooth reconstruction reports error rates approximately two times lower.
Conclusions:
Generative DL models analyze dental datasets to reconstruct missing teeth by extracting insights into patterns and structures. Through specialized application, these models reconstruct morphologically and functionally sound dental structures, leveraging information from the existing teeth. The reported advancements facilitate the feasibility of DL-based dental crown reconstruction. Beyond GANs and Transformers with point clouds or voxels, recent studies indicate promising outcomes with diffusion-based architectures and innovative data representations like wavelets for 3D shape completion and inference problems.
Clinical significance:
Generative network architectures employed in the analysis and reconstruction of dental structures demonstrate notable proficiency. The enhanced accuracy and efficiency of DL-based frameworks hold the potential to enhance clinical outcomes and increase patient satisfaction. The reduced reconstruction times and diminished requirement for manual intervention may lead to cost savings and improved accessibility of dental services.
Um den zunehmenden Anforderungen an die Beherrschung digitaler Techniken und an die Fähigkeit zur interdisziplinären Zusammenarbeit an Studierende aller Fachrichtungen zu begegnen wurde das interdisziplinäre Lehrformat Digitalisierungskollegs für Studierende entwickelt. Das in vielen Fachbereichen ausbaufähige Angebot von Digitalthemen in der Hoch- schullehre wird hiermit dauerhaft erweitert. Ein Digitalisierungskolleg besteht aus einer Vorlesungs- reihe mit angrenzendem Seminar, in denen Studierende interdisziplinäre Lösungen für Fragen der digitalen Transformation entwickeln. Geleitet werden sie von etablierten Wissenschaftlerinnen und Wissenschaftlern, aktiv betreut und ausgestaltet von ein bis zwei Coaches. Kernelement sowohl des Kollegs als auch der einzelnen Projekte ist die Interdisziplinarität. Eine*r der beteiligten Projekt- leiter*innen hat einen direkten Bezug zur Technik und kommt aus der Informatik, der Wirtschafts- informatik, der Elektrotechnik oder vergleichbaren Disziplinen. Zielgruppe der Projekte sind Studierende verschiedener Disziplinen im Masterstudium oder in den letzten Semestern eines Bachelorstudiums. Durch die Teilnahme erwerben auch Studierende aus digitalisierungsfernen Fächern frühzeitig umfangreiche IT-Kenntnisse. Als Begleiteffekt der umfangreichen Vernetzung zwischen den Digitalisierungskollegs (Studierende, Coaches und Projektleitende) entsteht bereits zu Beginn einer wissenschaftlichen Karriere eine große digitale Community. Alle Teilnehmenden lernen frühzeitig die interdisziplinäre Zusammenarbeit und verbessern erheblich ihre Karriere- chancen innerhalb und außerhalb der Wissenschaft.
Case study research is one of the most widely used research methods in Information Systems (IS). In recent years, an increasing number of publications have used case studies with few sources of evidence, such as single interviews per case. While there is much methodological guidance on rigorously conducting multiple case studies, it remains unclear how researchers can achieve an acceptable level of rigour for this emerging type of multiple case study with few sources of evidence, i.e., multiple mini case studies. In this context, we synthesise methodological guidance for multiple case study research from a cross-disciplinary perspective to develop an analytical framework. Furthermore, we calibrate this analytical framework to multiple mini case studies by reviewing previous IS publications that use multiple mini case studies to provide guidelines to conduct multiple mini case studies rigorously. We also offer a conceptual definition of multiple mini case studies, distinguish them from other research approaches, and position multiple mini case studies as a pragmatic and rigorous approach to research emerging and innovative phenomena in IS.
It remains difficult to segregate pelagic habitats since structuring processes are dynamic on a wide range of scales and clear boundaries in the open ocean are non-existent. However, to improve our knowledge about existing ecological niches and the processes shaping the enormous diversity of marine plankton, we need a better understanding of the driving forces behind plankton patchiness. Here we describe a new machine-learning method to detect and quantify pelagic habitats based on hydrographic measurements. An Autoencoder learns two-dimensional, meaningful representations of higher-dimensional micro-habitats, which are characterized by a variety of biotic and abiotic measurements from a high-speed ROTV. Subsequently, we apply a density-based clustering algorithm to group similar micro-habitats into associated pelagic macro-habitats in the German Bight of the North Sea. Three distinct macro-habitats, a “surface mixed layer,” a “bottom layer,” and an exceptionally “productive layer” are consistently identified, each with its distinct plankton community. We provide evidence that the model detects relevant features like the doming of the thermocline within an Offshore Wind Farm or the presence of a tidal mixing front.
Control and Automation of services of the urban infrastructure offered to citizens and tourists are elementary parts of a smart city. But both rely on a stable supply of data from sensors spread across the whole city, e. g., the fill level sensors of waste bins needed for a waste management tool which we developed in a collaboration with the Regensburg city council for the on-demand collection of waste bins. Europe has a lot of historic cities like Regensburg with narrow streets and huge building walls, some made from granite and fieldstones, which often represents an insurmountable obstacle to wireless data transmission. The reduction of the road traffic volume poses an additional challenge for city planners. By means of networked planning and simulation software, the situation, state and efficiency of citywide logistic services can be monitored and optimized. In the course of such optimizations, we propose the combination of digital and logistic services. As an example, we show that monitoring state information, such as the waste bin fill levels, can be accomplished using the same vehicles and the same planning software, that is used for luggage transportation. Moreover, we describe how we adapted a solver for a variant of the TSP, namely the prize-collecting traveling salesman, to optimize the route planning dynamically.
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 educational research, non-personalized learning content increases learners' cognitive load, causing them to lower their performance and sometimes drop out of the course. Personalizing learning content with learners’ unique characteristics, like learning styles, personality traits, and learning strategies, is being suggested to improve learners’ success. Several theories exist for assessing learners’ unique characteristics. By the end of 2020, 71 learning style theories have been formulated, and research has shown that combining multiple learning style theories to recommend learning paths yields better results. As of the end of 2022, there is no single research that demonstrates a relationship between the Index of Learning Styles (ILS) based Felder-Silverman learning style model (FSLSM) dimensions, Big Five (BFI-10) based personality traits, and the Learning strategies in studying (LIST-K) based learning strategies factors for personalizing learning content.
In this paper, an innovative approach is proposed to estimate the relationship between these theories and map the corresponding learning elements to create personalized learning paths. Respective questionnaires were distributed to 297 higher education students for data collection. A three-step approach was formulated to estimate the relationship between the models. First, a literature search was conducted to find existing studies. Then, an expert interview was carried out with a group of one software engineering education research professor, three doctoral students, and two master’s students. Finally, the correlations between the students' questionnaire responses were calculated. To achieve this, a Bayesian Network was built with expert knowledge from the three-step approach, and the weights were learned from collected data. The probability of individual FSLSM learning style dimensions was estimated for a new test sample. Based on the literature, the learning elements were mapped to the respective FSLSM learning style dimensions and were initiated as learning paths to the learners.
The next steps are proposed to extend this framework and dynamically recommend learning paths in real time. In addition, the individual levels of learning style dimensions, personality traits, and learning strategies can be considered to improve the recommendations. Further, using probabilities for mapping learning elements to learning styles can increase the chance of initiating multiple learning paths for an individual learner.
This paper presents the results of a data collection with the LIST-K questionnaire. This questionnaire measures students’ learning strategies and shows which strategies are particularly dominant or rather weak.
Learning strategies have long been a major area of research in educational science and psychology. In these disciplines, learning strategies are understood as intentional behaviors and cognitive skills that learners employ to effectively complete learning tasks, by selecting, acquiring, organizing, and integrating information into their existing knowledge for long-term retention.
The LIST-K, developed by Klingsieck in 2018, was chosen for accessing learning strategies due to its thematic suitability, widespread use, and test economy. It covers a total of four main categories (i.e., cognitive strategies, metacognitive strategies, management of internal resources, and management of external resources), each of which are subdivided into further subscales. With a total of 39 items answered via a 5-step Likert scale, the LIST-K can cover the topic relatively comprehensively and at the same time be completed in a reasonable amount of time of approximately 10 minutes.
The LIST-K was used as part of a combined data collection along with other questionnaires on their personal data, their preferences regarding certain learning elements, their learning style (i.e. the ILS), and personality (i.e. the BFI-10). A total of 207 students from different study programs participated via an online survey created using the survey tool "LimeSurvey". Participation in the study was voluntary, anonymously, and in compliance with the GDPR.
Overall, the results of the LIST-K show that students are willing to work intensively on relevant topics intensively and to perform beyond the requirements of the course seeking additional learning material. At the same time, however, it is apparent that the organization of their own learning process could still be improved. For example, students start repeating content too late (mean=2.70; SD=0.92) and do not set goals for themselves and do not create a learning plan (mean=3.19; SD=0.90). They also learn without a schedule (mean=2.23; SD=0.97) and miss opportunities to learn together with other students (mean=3.17; SD=0.94).
The findings of the data collection will be used to create an AI-based adaptive learning management system that will create individualized learning paths for students in their respective courses. From the results of the LIST-K, it appears that the adaptive learning management system should primarily support organizational aspects of student learning. Even small impulses (an individual schedule of when to learn what or a hierarchical structuring of the learning material) could help students to complete their courses more successfully and improve their learning.
Eye tracking has proven to be a powerful tool in a variety of empirical research areas; hence, it is steadily gaining attention. Driven by the expanding frontiers of Artificial Intelligence and its potential for data analysis, eye tracking technology offers promising applications in diverse fields, from usability research to cognitive research. The education sector in particular can benefit from the increased use of eye tracking technology - both indirectly, for example by studying the differences in gaze patterns between experts and novices to identify promising strategies, and directly by using the technology itself to teach in future classrooms.
As with any empirical method, the results depend directly on the quality of the data collected. That raises the question of which parameters educators or researchers can influence to maximize the data quality of an eye tracker. This is the starting point of the present work: In an empirical study of eye tracking as an (educational) technology, we systematically examine factors that influence the data quality, such as illumination, sampling frequency, and head orientation - parameters that can be varied without much additional effort in everyday classroom or research use - using two human subjects, an artificial face, and the Tobii Pro Spectrum.
We rely on metrics derived from the raw gaze data, such as accuracy or precision, to measure data quality. The obtained results derive practical advice for educators and researchers, such as using the lowest sampling frequency appropriate for a certain purpose. Thereby, this research fills a gap in the current understanding of eye tracker performance and, by offering best practices, enables researchers or teachers to produce data of the highest quality possible and therefore best results when using eye trackers in laboratories or future classrooms.
Universities are faced with a rising number of dropouts in recent years. This is largely due to students' limited capability of finding individual learning paths through various course materials. However, a possible solution to this problem is the introduction of adaptive learning management systems, which recommend tailored learning paths to students – based on their individual learning styles. For the classification of learning styles, the most commonly used methods are questionnaires and learning analytics. Nevertheless, both methods are prone to errors: questionnaires may give superficial answers due to lack of time or motivation, while learning analytics do not reflect offline learning behavior. This paper proposes an alternative approach to classify students' learning styles by integrating eye tracking in combination with Machine Learning (ML) algorithms.
Incorporating eye tracking technology into the classification process eliminates the potential problems arising from questionnaires or learning analytics by providing a more objective and detailed analysis of the subject's behavior. Moreover, this approach allows for a deeper understanding of subconscious processes and provides valuable insights into the individualized learning preferences of students.
In order to demonstrate this approach, an eye tracking study is conducted with 117 participants using the Tobii Pro Fusion. Using qualitative and quantitative analyses, certain patterns in the subjects' gaze behavior are assigned to their learning styles given by the validated Index of Learning Styles (ILS) questionnaire.
In short, this paper presents an innovative solution to the challenges associated with classifying students' learning styles. By combining eye tracking data with ML algorithms, an accurate and insightful understanding of students' individual learning paths can be achieved, ultimately leading to improved educational outcomes and reduced dropout rates.
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.
Control Oriented Mathematical Modeling of a Bidirectional DC-DC Converter - Part 1: Buck Mode
(2023)
Parallel connection of different batteries equipped with bidirectional DC-DC converters offers an increase of the total storage capacity, the provision of higher currents and an improvement of reliability and system availability. To share the load current among the DC-DC converters while maintaining the safe operating range of the batteries, appropriate controllers are needed. The basis for the design of these control approaches requires knowledge of both the static and dynamic characteristics of the DC-DC converter used. In this paper, the small signal analysis of a DC-DC converter in buck mode is shown using the circuit averaging technique. The paper gives an overview of all required transfer functions:. The control and line to output transfer functions for CCM and DCM relevant for average current mode control as well as for voltage control are derived and their poles and zeros are determined. This provides the basis for stability consideration, analysis of the overall control structure and controller design.
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.
In modern vehicles, system complexity and technical capabilities are constantly growing. As a result, manufacturers and regulators are both increasingly challenged to ensure the reliability, safety, and intended behavior of these systems. With current methodologies, it is difficult to address the various interactions between vehicle components and environmental factors. However, model-based engineering offers a solution by allowing to abstract reality and enhancing communication among engineers and stakeholders. Applying this method requires a model format that is machine-processable, human-understandable, and mathematically sound. In addition, the model format needs to support probabilistic reasoning to account for incomplete data and knowledge about a problem domain. We propose structural causal models as a suitable framework for addressing these demands. In this article, we show how to combine data from different sources into an inferable causal model for an advanced driver-assistance system. We then consider the developed causal model for scenario-based testing to illustrate how a model-based approach can improve industrial system development processes. We conclude this paper by discussing the ongoing challenges to our approach and provide pointers for future work.
In a distributed system, functionally equivalent nodes work together to form a system with improved availability, reliability and fault tolerance. Thereby, the purpose is to achieve a common control objective. As multiple components cooperate to accomplish tasks, coordination between them is required. Electing a node as the temporary leader can be a possible solution to perform coordination. This work presents a self-stabilizing algorithm for the election of a leader in dynamically reconfigurable bus topology-based broadcast systems with a message and time complexity of O(1). The election is performed dynamically, i.e., not only when the leader node fails, and is criterion-based. The criterion used is a performance related value which evaluates the properties of the node regarding the ability to perform the tasks of the leader. The increased demands on the leader are taken into account and a re-election is started when the criterion value drops below a predefined level. The goal here is to distribute the load more evenly and to reduce the probability of failure due to overload of individual nodes. For improved system availability and reduced fault rates, a management level consisting of leader, assistant and co-assistant is introduced. This reduces the number of required messages and the duration in case of non-initial election. For further reduction of required messages to uniquely determine a leader, the CAN protocol is exploited. The proposed algorithm selects a node with an improved failure rate and a reduced message and hence time complexity while satisfying the safety and termination constraints. The operation of the algorithm is validated using a hardware test setup.
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.
An immense diversity in bottle types requires high accuracy during sorting for recycling purposes by breweries. This extremely complex and time-consuming procedure can result in enormous additional costs for them. This paper presents transfer learning-based algorithms for classifying beer bottle brands using camera images, applicable in individual sorting solutions for different use cases. The problem is tackled using customised EfficientNet, InceptionResNet and VGG models along with an augmented dataset. In addition, a detailed analysis of different model and parameter combinations is performed, enabling tailor-made technologies for specific conditions and resource limitations. In accompanying validations and subsequent tests, a test accuracy of 100% in the recognition of beer brands could be achieved, proving the proposed method fully contributes to the solution of the problem.
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.
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.
This study uses holistic models of image perception to analyze and interpret eye movements during a code review. 23 participants (15 novices and 8 experts) take part in the experiment. The subjects’ task is to review six short code examples in C programming language and identify possible errors. During the experiment, their eye movements are recorded by an SMI 250 REDmobile. Additional data is collected through questionnaires and retrospective interviews. The results implicate that holistic models of image perception provide a suitable theoretical background for the analysis and interpretation of eye movements during code reviews. The assumptions of these models are particularly evident for expert programmers. Their approach can be divided into different phases with characteristic eye movement patterns. It is best described as switching between scans of the code example (global viewing) and the detailed examination of errors (focal viewing).
Nowadays, learning management systems are widely employed in all educational institutions to instruct students as a result of the increasing in online usage. Today’s learning management systems provide learning paths without personalizing them to the characteristics of the learner. Therefore, research these days is concentrated on employing AI-based strategies to personalize the systems. However, there are many different AI algorithms, making it challenging to determine which ones are most suited for taking into account the many different features of learner data and learning contents. This paper conducts a systematic literature review in order to discuss the AI-based methods that are frequently used to identify learner characteristics, organize the learning contents, recommend learning paths, and highlight their advantages and disadvantages.
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 humans, we tend to use models to describe reality. Modeling languages provide the formal frameworks for creating such models. Usually, the graphical design of individual model elements is based on subjective decisions; their suitability is determined at most by the prevalence of the modeling language. With other words: there is no objective way to compare different designs of model elements. The present paper addresses this issue: it introduces a systematic approach for evaluating the elements of graph-based modeling languages comprising 14 criteria – derived from standards, usability analyses, or the design theories ‘Physics of Notations’ and ‘Cognitive Dimensions of Notations’. The criteria come with measurement procedures and evaluation schemes based on reasoning, eye tracking, and questioning. The developed approach is demonstrated with a specific use case: three distinct sets of node elements for causal graphs are evaluated in an eye tracking study with 41 subjects.
The dropout rate at universities has been very high for years. Thereby, the inexperience and lack of knowledge of students in dealing with individual learning paths in various courses of study plays a decisive role. Adaptive learning management systems are suitable countermeasures, in which learners’ learning styles are classified using questionnaires or computationally intensive algorithms before a learning path is suggested accordingly. In this paper, a study design for student learning style classification using eye tracking is presented. Furthermore, qualitative and quantitative analyses clarify certain relationships between students’ eye movements and learning styles. With the help of classification based on eye tracking, the filling out of questionnaires or the integration of computationally or cost-intensive algorithms can be made redundant in the future.
This paper assesses the relation between personality, demographics, and learning style. Hence, data is collected from 200 participants using 1) the BFI-10 to obtain the participant’s expression of personality traits according to the five-factor model, 2) the ILS to determine the participant’s learning style according to Felder and Silverman, and 3) a demographic questionnaire. From the obtained data, we train and evaluate a Bayesian network. Using Bayesian statistics, we show that age and gender slightly influence personality and that demographics as well as personality have at least a minor effect on learning styles. We also discuss the limitations and future work of the presented approach.
This study examines how Klingsieck’s LIST-K questionnaire [22] can be shortened and adapted to the requirements of an online learning management system. In a study with 213 participants, the questionnaire is subjected to an exploitative factor analysis. In a next step, the results are evaluated in terms of their reliability. This process creates a modified factor structure for the LIST-K, comprising a total of eight factors. The reliability of the modified questionnaire is at an α of .770. The shortened version of the LIST-K questionnaire is currently being used on an experimental basis in different courses.
C is one of the most widely used programming languages - MISRA C is one of the most known sets of coding guidelines for C. This paper examines the usefulness and comprehensibility of the MISRA C:2012 guidelines in an eye tracking study. There, subjects encounter non-compliant code in four different code review settings: with no additional reference, with an actual MISRA C guideline, with a case-specific interpretation of a MISRA C guideline, and with a compliant version of the code. The data collected was analyzed not only in terms of the four presentation styles, but also by dividing the subjects into experience levels based on their semesters of study or years of work experience. Regarding the difference between actual and interpreted guidelines, we found that for interpreted guidelines the error detection rate is higher whereas the duration and frequency of visits to the guideline itself are mainly lower. This suggest that the actual guidelines are less useful and more difficult to understand. The former is contradicted by the subjects’ opinions: when surveyed, they rated the usefulness of the actual guidelines higher.
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.
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.