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Bisherige Versuche der HCI-Community die Lebensmittelverschwendung oder den CO2-Fußabdruck zu reduzieren, basierten meist auf Persuasive Design Ansätzen. Diese werden jedoch kritisiert, die Alltagswelten und Konsumpraktiken für eine Langzeitwirkung nur unzureichend zu berücksichtigen. Das Problem aufgreifend, untersucht dieser Beitrag die Rolle (digitaler) Medien im Übergang zu einer veganen Ernährungspraktik. Hierfür wurden semi-strukturierte Interviews mit 9 VeganerInnen geführt und vor dem Hintergrund der Praxistheorie analysiert. Die Ergebnisse deuten dabei auf eine intensive Nutzung (digitaler) Medien, insbesondere in der frühen Phase der Änderung der Konsumpraktik. Statt Gamification oder Persuasive Design, zeigt sich Mediennutzung in Form von Irritation, Informationsbereitstellung zur Ausbildung eines vegan-spezifischen Konsumwissens sowie als Vermittler zwischen Gleichgesinnten.
The alternative use of travel time is one of the widely discussed benefits of driverless cars. We therefore conducted 14 co-design sessions to examine how people manage their time, to determine how they perceive the value of time in driverless cars and to derive design implications. Our findings suggest that driverless mobility will affect both people’s use of travel time as well as their time management in general. The participants repeatedly stated the desire of completing tasks while traveling to save time for activities that are normally neglected in their everyday life. Using travel time efficiently requires using car space efficiently, too. We found out that the design concept of tiny houses could serve as common design pattern to deal with the limited space within cars and support diverse needs.
Trust is the lubricant of the sharing economy, especially in peer-to-peer carsharing where you leave a valuable good to a stranger in the hope of getting it backunscathed. Central mechanisms for handling this information gap nowadays are ratings and reviews of other users. The rising of connected car technology opens new possibilities to increase trust by collecting and providing e.g. driving behavior data. At the same time, this means an intrusion into the privacy of the user. Therefore, in this work we explore technological approaches that allow building trust without violating the privacy of individuals. We evaluate to what extent blockchain technology and smart contracts are suitable technologies to meet these challengesby setting upa prototype implementation of a block-chain-based carsharing approach. In this context, we present our research approachand evaluate the prototype in terms of trust and privacy.
The paper presents the topological reduction method applied to gas transport networks, using contraction of series, parallel and tree-like subgraphs. The contraction operations are implemented for pipe elements, described by quadratic friction law. This allows significant reduction of the graphs and acceleration of solution procedure for stationary network problems. The algorithm has been tested on several realistic network examples. The possible extensions of the method to different friction laws and other elements are discussed.
The Potential of Sustainable Antimicrobial Additives for Food Packaging from Native Plants in Benin
(2019)
The Learning Culture Survey (LCS) is a questionnaire-based research, investigating students’ perceptions of and expectations towards Higher Education (HE). The aim of this survey is to improve our understanding about the sources of cultural conflicts in educational scenarios. This understanding, shell help us to predict potential conflict situations and develop supportive measures.
After three years of development, the LCS was initialized in 2010 in South Korea and Germany. During the following years, the investigations were extended to further countries. The results, on the one hand, provided insights about the cultural context of HE in general and on the other hand, about specific (national / regional) characteristics of learners in HE. Most issues targeted with the questionnaire were directly linked to value systems. Thus, we expected from the beginning that the collected data would keep valid over longer periods of time. However, we had no evidence regarding the actual persistence of learning culture. For a study, designed to being implemented on a global scope and providing input for further applications, persistence is a basic condition to justify related investigations.
To answer the question on persistence, we repeated the LCS in our university every four years, between 2010 to 2018/19. Besides a small number of slight changes, explainable out of their situational context, the overall results kept consistent over the investigated years. In this paper, after an introduction of the LCS’ concept, setting and its general results from the past years, we present the insights from our most recently finalized longitudinal study on learning culture.
The need for innovation around the control functions of inverters is great. PV inverters were initially expected to be passive followers of the grid and to disconnect as soon as abnormal conditions happened. Since future power systems will be dominated by generation and storage resources interfaced through inverters these converters must move from following to forming and sustaining the grid. As “digital natives” PV inverters can also play an important role in the digitalisation of distribution networks. In this short review we identified a large potential to make the PV inverter the smart local hub in a distributed energy system. At the micro level, costs and coordination can be improved with bidirectional inverters between the AC grid and PV production, stationary storage, car chargers and DC loads. At the macro level the distributed nature of PV generation means that the same devices will support both to the local distribution network and to the global stability of the grid. Much success has been obtained in the former. The later remains a challenge, in particular in terms of scaling. Yet there is some urgency in researching and demonstrating such solutions. And while digitalisation offers promise in all control aspects it also raises significant cybersecurity concerns.
Tell Your Robot What To Do: Evaluation of Natural Language Models for Robot Command Processing
(2019)
The use of natural language to indicate robot tasks is a convenient way to command robots. As a result, several models and approaches capable of understanding robot commands have been developed, which however complicates the choice of a suitable model for a given scenario. In this work, we present a comparative analysis and benchmarking of four natural language understanding models - Mbot, Rasa, LU4R, and ECG. We particularly evaluate the performance of the models to understand domestic service robot commands by recognizing the actions and any complementary information in them in three use cases: the RoboCup@Home General Purpose Service Robot (GPSR) category 1 contest, GPSR category 2, and hospital logistics in the context of the ROPOD project.
Synthesis of Substituted Hydroxyapatite for Application in Bone Tissue Engineering and Drug Delivery
(2019)
In the field of service robots, dealing with faults is crucial to promote user acceptance. In this context, this work focuses on some specific faults which arise from the interaction of a robot with its real world environment due to insufficient knowledge for action execution.
In our previous work [1], we have shown that such missing knowledge can be obtained through learning by experimentation. The combination of symbolic and geometric models allows us to represent action execution knowledge effectively. However we did not propose a suitable representation of the symbolic model.
In this work we investigate such symbolic representation and evaluate its learning capability. The experimental analysis is performed on four use cases using four different learning paradigms. As a result, the symbolic representation together with the most suitable learning paradigm are identified.
Stakeholder-Analyse zum Einsatz IIoT-basierter Frischeinformationen in der Lebensmittelindustrie
(2019)
Eine Herausforderung bei der Implementierung des industriellen Internet of Things (IIoT) besteht darin, Mehrwerte in Wertschöpfungsketten zu identifizieren, um darauf aufbauend Lösungen nutzerzentriert zu gestalten. Dieser Beitrag stellt das Forschungsprojekt FreshIndex vor, bei dem diese Herausforderung durch eine Kombination aus Stakeholder-Analyse und User-Centered-Design-Methoden adressiert wurde. Ziel des Projekts ist es, eine IIoT-basierte Lösung zum Monitoring der Kühlkette in der Lebensmittelindustrie zu entwickeln. Hierzu ist es wichtig zu wissen, welche Nutzer/-innen mit den Daten in Berührung kommen und welche Erfahrungen, Fähigkeiten, Anforderungen und Wünsche sie mitbringen. Die Berücksichtigung dieser Aspekte ist relevant für den Erfolg der Konzeption, Implementierung und des Betriebs eines IIoT-Systems. So können nützliche und handhabbare Produktideen generiert und Anwendungen gestaltet werden, die von Mitarbeiter/-innen und Konsument/-innen angenommen werden. IIoT schließt somit die lokale Verwendbarkeit von Daten entlang der Wertschöpfungskette ein und beschränkt sich nicht auf zentrale Verfügbarkeit von Daten.
Verschiedene intelligente Heimautomatisierungsgeräte wie Lampen, Schlösser und Thermostate verbreiten sich rasant im privaten Umfeld. Ein typisches Kommunikationsprotokoll für diese Geräteklasse ist Bluetooth Low Energy (BLE). In dieser Arbeit wird eine strukturierte Sicherheitsanalyse für BLE vorgestellt. Die beschriebene Vorgehensweise kategorisiert bekannte Angriffsvektoren und beschreibt einen möglichen Aufbau für eine Analyse. Im Zuge dieser Arbeit wurden einige sicherheitsrelevante Probleme aufgedeckt, die es Angreifern ermöglichen die Geräte vollständig zu übernehmen. Es zeigte sich, dass im Standard vorgesehene Sicherheitsfunktionen wie Verschlüsselung und Integritätsprüfungen häufig gar nicht oder fehlerhaft implementiert sind.
When developing robot functionalities, finite state machines are commonly used due to their straightforward semantics and simple implementation. State machines are also a natural implementation choice when designing robot experiments, as they generally lead to reproducible program execution. In practice, the implementation of state machines can lead to significant code repetition and may necessitate unnecessary code interaction when reparameterisation is required. In this paper, we present a small Python library that allows state machines to be specified, configured, and dynamically created using a minimal domain-specific language. We illustrate the use of the library in three different use cases - scenario definition in the context of the RoboCup@Home competition, experiment design in the context of the ROPOD project, as well as specification transfer between robots.
Emotion and gender recognition from facial features are important properties of human empathy. Robots should also have these capabilities. For this purpose we have designed special convolutional modules that allow a model to recognize emotions and gender with a considerable lower number of parameters, enabling real-time evaluation on a constrained platform. We report accuracies of 96% in the IMDB gender dataset and 66% in the FER-2013 emotion dataset, while requiring a computation time of less than 0.008 seconds on a Core i7 CPU. All our code, demos and pre-trained architectures have been released under an open-source license in our repository at https://github.com/oarriaga/face classification.
Quantifying Interference in WiLD Networks using Topography Data and Realistic Antenna Patterns
(2019)
Avoiding possible interference is a key aspect to maximize the performance in Wi-Fi based Long Distance networks. In this paper we quantify self-induced interference based on data derived from our testbed and match the findings against simulations. By enhancing current simulation models with two key elements we significantly reduce the deviation between testbed and simulation: the usage of detailed antenna patterns compared to the cone model and propagation modeling enhanced by license-free topography data. Based on the gathered data we discuss several possible optimization approaches such as physical separation of local radios, tuning the sensitivity of the transmitter and using centralized compared to distributed channel assignment algorithms. While our testbed is based on 5 GHz Wi-Fi, we briefly discuss the possible impact of our results to other frequency bands.
Surrogate models are used to reduce the burden of expensive-to-evaluate objective functions in optimization. By creating models which map genomes to objective values, these models can estimate the performance of unknown inputs, and so be used in place of expensive objective functions. Evolutionary techniques such as genetic programming or neuroevolution commonly alter the structure of the genome itself. A lack of consistency in the genotype is a fatal blow to data-driven modeling techniques: interpolation between points is impossible without a common input space. However, while the dimensionality of genotypes may differ across individuals, in many domains, such as controllers or classifiers, the dimensionality of the input and output remains constant. In this work we leverage this insight to embed differing neural networks into the same input space. To judge the difference between the behavior of two neural networks, we give them both the same input sequence, and examine the difference in output. This difference, the phenotypic distance, can then be used to situate these networks into a common input space, allowing us to produce surrogate models which can predict the performance of neural networks regardless of topology. In a robotic navigation task, we show that models trained using this phenotypic embedding perform as well or better as those trained on the weight values of a fixed topology neural network. We establish such phenotypic surrogate models as a promising and flexible approach which enables surrogate modeling even for representations that undergo structural changes.