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Embedded smart home — remote lab MOOC with optional real hardware experience for over 4000 students
(2018)
MOOCs (Massive Open Online Courses) become more and more popular for learners of all ages to study further or to learn new subjects of interest. The purpose of this paper is to introduce a different MOOC course style. Typically, video content is shown teaching the student new information. After watching a video, self-test questions can be answered. Finally, the student answers weekly exams and final exams like the self test questions. Out of the points that have been scored for weekly and final exams a certificate can be issued. Our approach extends the possibility to receive points for the final score with practical programming exercises on real hardware. It allows the student to do embedded programming by communicating over GPIO pins to control LEDs and measure sensor values. Additionally, they can visualize values on an embedded display using web technologies, which are an essential part of embedded and smart home devices to communicate with common APIs. Students have the opportunity to solve all tasks within the online remote lab and at home on the same kind of hardware. The evaluation of this MOOCs indicates the interesting design for students to learn an engineering technique with new technology approaches in an appropriate, modern, supporting and motivating way of teaching.
When students watch learning videos online, they usually need to watch several hours of video content. In the end, not every minute of a video is relevant for the exam. Additionally, students need to add notes to clarify issues of a lecture. There are several possibilities to enhance the metadata of a video, e.g. a typical way to add user-specific information to an online video is a comment functionality, which allows users to share their thoughts and questions with the public. In contrast to common video material which can be found online, lecture videos are used for exam preparation. Due to this difference, the idea comes up to annotate lecture videos with markers and personal notes for a better understanding of the taught content. Especially, students learning for an exam use their notes to refresh their memories. To ease this learning method with lecture videos, we introduce the annotation feature in our video lecture archive. This functionality supports the students with keeping track of their thoughts by providing an intuitive interface to easily add, modify or remove their ideas. This annotation function is integrated in the video player. Hence, scrolling to a separate annotation area on the website is not necessary. Furthermore, the annotated notes can be exported together with the slide content to a PDF file, which can then be printed easily. Lecture video annotations support and motivate students to learn and watch videos from an E-Learning video archive.
In den letzten Jahren ist die Aufnahme und Verbreitung von Videos immer einfacher geworden. Daher sind die Relevanz und Beliebtheit zur Aufnahme von Vorlesungsvideos in den letzten Jahren stark angestiegen. Dies führt zu einem großen Datenbestand an Vorlesungsvideos in den Video-Vorlesungsarchiven der Universitäten. Durch diesen wachsenden Datenbestand wird es allerdings für die Studenten immer schwieriger, die relevanten Videos eines Vorlesungsarchivs aufzufinden. Zusätzlich haben viele Lerninteressierte durch ihre alltägliche Arbeit und familiären Verpflichtungen immer weniger Zeit sich mit dem Lernen zu beschäftigen. Ein weiterer Aspekt, der das Lernen im Internet erschwert, ist, dass es durch soziale Netzwerke und anderen Online-Plattformen vielfältige Ablenkungsmöglichkeiten gibt. Daher ist das Ziel dieser Arbeit, Möglichkeiten aufzuzeigen, welche das E-Learning bieten kann, um Nutzer beim Lernprozess zu unterstützen und zu motivieren.
Das Hauptkonzept zur Unterstützung der Studenten ist das präzise Auffinden von Informationen in den immer weiter wachsenden Vorlesungsvideoarchiven. Dazu werden die Vorlesungen im Voraus analysiert und die Texte der Vorlesungsfolien mit verschiedenen Methoden indexiert. Daraufhin können die Studenten mit der Suche oder dem Lecture-Butler Lerninhalte entsprechend Ihres aktuellen Wissensstandes auffinden. Die möglichen verwendeten Technologien für das Auffinden wurden, sowohl technisch, als auch durch Studentenumfragen erfolgreich evaluiert. Zur Motivation von Studenten in Vorlesungsarchiven werden diverse Konzepte betrachtet und die Umsetzung evaluiert, die den Studenten interaktiv in den Lernprozess einbeziehen.
Neben Vorlesungsarchiven existieren sowohl im privaten als auch im dienstlichen Weiterbildungsbereich die in den letzten Jahren immer beliebter werdenden MOOCs. Generell sind die Abschlussquoten von MOOCs allerdings mit durchschnittlich 7% eher gering. Daher werden Motivationslösungen für MOOCs im Bereich von eingebetteten Systemen betrachtet, die in praktischen Programmierkursen Anwendung finden. Zusätzlich wurden Kurse evaluiert, welche die Programmierung von eingebetteten Systemen behandeln. Die Verfügbarkeit war bei Kursen von bis zu 10.000 eingeschriebenen Teilnehmern hierbei kein schwerwiegendes Problem. Die Verwendung von eingebetteten Systemen in Programmierkursen sind bei den Studenten in der praktischen Umsetzung auf sehr großes Interesse gestoßen.
CurEx
(2018)
The integration of diverse structured and unstructured information sources into a unified, domain-specific knowledge base is an important task in many areas. A well-maintained knowledge base enables data analysis in complex scenarios, such as risk analysis in the financial sector or investigating large data leaks, such as the Paradise or Panama papers. Both the creation of such knowledge bases, as well as their continuous maintenance and curation involves many complex tasks and considerable manual effort. With CurEx, we present a modular system that allows structured and unstructured data sources to be integrated into a domain-specific knowledge base. In particular, we (i) enable the incremental improvement of each individual integration component; (ii) enable the selective generation of multiple knowledge graphs from the information contained in the knowledge base; and (iii) provide two distinct user interfaces tailored to the needs of data engineers and end-users respectively. The former has curation capabilities and controls the integration process, whereas the latter focuses on the exploration of the generated knowledge graph.
Modern knowledge bases contain and organize knowledge from many different topic areas. Apart from specific entity information, they also store information about their relationships amongst each other. Combining this information results in a knowledge graph that can be particularly helpful in cases where relationships are of central importance. Among other applications, modern risk assessment in the financial sector can benefit from the inherent network structure of such knowledge graphs by assessing the consequences and risks of certain events, such as corporate insolvencies or fraudulent behavior, based on the underlying network structure. As public knowledge bases often do not contain the necessary information for the analysis of such scenarios, the need arises to create and maintain dedicated domain-specific knowledge bases.
This thesis investigates the process of creating domain-specific knowledge bases from structured and unstructured data sources. In particular, it addresses the topics of named entity recognition (NER), duplicate detection, and knowledge validation, which represent essential steps in the construction of knowledge bases.
As such, we present a novel method for duplicate detection based on a Siamese neural network that is able to learn a dataset-specific similarity measure which is used to identify duplicates. Using the specialized network architecture, we design and implement a knowledge transfer between two deduplication networks, which leads to significant performance improvements and a reduction of required training data.
Furthermore, we propose a named entity recognition approach that is able to identify company names by integrating external knowledge in the form of dictionaries into the training process of a conditional random field classifier. In this context, we study the effects of different dictionaries on the performance of the NER classifier. We show that both the inclusion of domain knowledge as well as the generation and use of alias names results in significant performance improvements.
For the validation of knowledge represented in a knowledge base, we introduce Colt, a framework for knowledge validation based on the interactive quality assessment of logical rules. In its most expressive implementation, we combine Gaussian processes with neural networks to create Colt-GP, an interactive algorithm for learning rule models. Unlike other approaches, Colt-GP uses knowledge graph embeddings and user feedback to cope with data quality issues of knowledge bases. The learned rule model can be used to conditionally apply a rule and assess its quality.
Finally, we present CurEx, a prototypical system for building domain-specific knowledge bases from structured and unstructured data sources. Its modular design is based on scalable technologies, which, in addition to processing large datasets, ensures that the modules can be easily exchanged or extended. CurEx offers multiple user interfaces, each tailored to the individual needs of a specific user group and is fully compatible with the Colt framework, which can be used as part of the system.
We conduct a wide range of experiments with different datasets to determine the strengths and weaknesses of the proposed methods. To ensure the validity of our results, we compare the proposed methods with competing approaches.
The dynamic landscape of digital transformation entails an impact on industrial-age manufacturing companies that goes beyond product offerings, changing operational paradigms, and requiring an organization-wide metamorphosis. An initiative to address the given challenges is the creation of Digital Innovation Units (DIUs) – departments or distinct legal entities that use new structures and practices to develop digital products, services, and business models and support or drive incumbents’ digital transformation. With more than 300 units in German-speaking countries alone and an increasing number of scientific publications, DIUs have become a widespread phenomenon in both research and practice.
This dissertation examines the evolution process of DIUs in the manufacturing
industry during their first three years of operation, through an extensive longitudinal single-case study and several cross-case syntheses of seven DIUs. Building on the lenses of organizational change and development, time, and socio-technical systems, this research provides insights into the fundamentals, temporal dynamics, socio-technical interactions, and relational dynamics of a DIU’s evolution process. Thus, the dissertation promotes a dynamic understanding of DIUs and adds a two-dimensional perspective to the often one-dimensional view of these units and their interactions with the main organization throughout the startup and growth phases of a DIU.
Furthermore, the dissertation constructs a phase model that depicts the early stages of DIU evolution based on these findings and by incorporating literature from information systems research. As a result, it illustrates the progressive intensification of collaboration between the DIU and the main organization. After being implemented, the DIU sparks initial collaboration and instigates change within (parts of) the main organization. Over time, it adapts to the corporate environment to some extent, responding to changing circumstances in order to contribute to long-term transformation. Temporally, the DIU drives the early phases of cooperation and adaptation in particular, while the main organization triggers the first major evolutionary step and realignment of the DIU.
Overall, the thesis identifies DIUs as malleable organizational structures that are crucial for digital transformation. Moreover, it provides guidance for practitioners on the process of building a new DIU from scratch or optimizing an existing one.
Challenges and proposals for introducing digital certificates in higher education infrastructures
(2023)
Questions about the recognition of MOOCs within and outside higher education were already being raised in the early 2010s. Today, recognition decisions are still made more or less on a case-by-case basis. However, digital certification approaches are now emerging that could automate recognition processes. The technical development of the required machinereadable documents and infrastructures is already well advanced in some cases. The DigiCerts consortium has developed a solution based on a collective blockchain. There are ongoing and open discussions regarding the particular technology, but the institutional implementation of digital certificates raises further questions. A number of workshops have been held at the Institute for Interactive Systems at Technische Hochschule Lübeck, which have identified the need for new responsibilities for issuing certificates. It has also become clear that all members of higher education institutions need to develop skills in the use of digital certificates.
How can interactive devices connect with users in the most immediate and intimate way? This question has driven interactive computing for decades. Throughout the last decades, we witnessed how mobile devices moved computing into users’ pockets, and recently, wearables put computing in constant physical contact with the user’s skin. In both cases moving the devices closer to users allowed devices to sense more of the user, and thus act more personal. The main question that drives our research is: what is the next logical step?
Some researchers argue that the next generation of interactive devices will move past the user’s skin and be directly implanted inside the user’s body. This has already happened in that we have pacemakers, insulin pumps, etc. However, we argue that what we see is not devices moving towards the inside of the user’s body, but rather towards the body’s biological “interface” they need to address in order to perform their function.
To implement our vision, we created a set of devices that intentionally borrow parts of the user’s body for input and output, rather than adding more technology to the body.
In this dissertation we present one specific flavor of such devices, i.e., devices that borrow the user’s muscles. We engineered I/O devices that interact with the user by reading and controlling muscle activity. To achieve the latter, our devices are based on medical-grade signal generators and electrodes attached to the user’s skin that send electrical impulses to the user’s muscles; these impulses then cause the user’s muscles to contract.
While electrical muscle stimulation (EMS) devices have been used to regenerate lost motor functions in rehabilitation medicine since the 1960s, in this dissertation, we propose a new perspective: EMS as a means for creating interactive systems.
We start by presenting seven prototypes of interactive devices that we have created to illustrate several benefits of EMS. These devices form two main categories: (1) Devices that allow users eyes-free access to information by means of their proprioceptive sense, such as the value of a variable in a computer system, a tool, or a plot; (2) Devices that increase immersion in virtual reality by simulating large forces, such as wind, physical impact, or walls and heavy objects.
Then, we analyze the potential of EMS to build interactive systems that miniaturize well and discuss how they leverage our proprioceptive sense as an I/O modality. We proceed by laying out the benefits and disadvantages of both EMS and mechanical haptic devices, such as exoskeletons.
We conclude by sketching an outline for future research on EMS by listing open technical, ethical and philosophical questions that we left unanswered.
Most machine learning methods provide only point estimates when being queried to predict on new data. This is problematic when the data is corrupted by noise, e.g. from imperfect measurements, or when the queried data point is very different to the data that the machine learning model has been trained with. Probabilistic modelling in machine learning naturally equips predictions with corresponding uncertainty estimates which allows a practitioner to incorporate information about measurement noise into the modelling process and to know when not to trust the predictions. A well-understood, flexible probabilistic framework is provided by Gaussian processes that are ideal as building blocks of probabilistic models. They lend themself naturally to the problem of regression, i.e., being given a set of inputs and corresponding observations and then predicting likely observations for new unseen inputs, and can also be adapted to many more machine learning tasks. However, exactly inferring the optimal parameters of such a Gaussian process model (in a computationally tractable manner) is only possible for regression tasks in small data regimes. Otherwise, approximate inference methods are needed, the most prominent of which is variational inference.
In this dissertation we study models that are composed of Gaussian processes embedded in other models in order to make those more flexible and/or probabilistic. The first example are deep Gaussian processes which can be thought of as a small network of Gaussian processes and which can be employed for flexible regression. The second model class that we study are Gaussian process state-space models. These can be used for time-series modelling, i.e., the task of being given a stream of data ordered by time and then predicting future observations. For both model classes the state-of-the-art approaches offer a trade-off between expressive models and computational properties (e.g. speed or convergence properties) and mostly employ variational inference. Our goal is to improve inference in both models by first getting a deep understanding of the existing methods and then, based on this, to design better inference methods. We achieve this by either exploring the existing trade-offs or by providing general improvements applicable to multiple methods.
We first provide an extensive background, introducing Gaussian processes and their sparse (approximate and efficient) variants. We continue with a description of the models under consideration in this thesis, deep Gaussian processes and Gaussian process state-space models, including detailed derivations and a theoretical comparison of existing methods.
Then we start analysing deep Gaussian processes more closely: Trading off the properties (good optimisation versus expressivity) of state-of-the-art methods in this field, we propose a new variational inference based approach. We then demonstrate experimentally that our new algorithm leads to better calibrated uncertainty estimates than existing methods.
Next, we turn our attention to Gaussian process state-space models, where we closely analyse the theoretical properties of existing methods.The understanding gained in this process leads us to propose a new inference scheme for general Gaussian process state-space models that incorporates effects on multiple time scales. This method is more efficient than previous approaches for long timeseries and outperforms its comparison partners on data sets in which effects on multiple time scales (fast and slowly varying dynamics) are present.
Finally, we propose a new inference approach for Gaussian process state-space models that trades off the properties of state-of-the-art methods in this field. By combining variational inference with another approximate inference method, the Laplace approximation, we design an efficient algorithm that outperforms its comparison partners since it achieves better calibrated uncertainties.
OpenLL
(2018)
Today's rendering APIs lack robust functionality and capabilities for dynamic, real-time text rendering and labeling, which represent key requirements for 3D application design in many fields. As a consequence, most rendering systems are barely or not at all equipped with respective capabilities. This paper drafts the unified text rendering and labeling API OpenLL intended to complement common rendering APIs, frameworks, and transmission formats. For it, various uses of static and dynamic placement of labels are showcased and a text interaction technique is presented. Furthermore, API design constraints with respect to state-of-the-art text rendering techniques are discussed. This contribution is intended to initiate a community-driven specification of a free and open label library.