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Validierung einer Web-Applikation zum Fern-Monitoring von Belastungs- und Erholungsparametern
(2020)
Simultan zur agilen Entwicklung einer Web-Applikation, die Parameter der Belastungs- und Beanspruchungssteuerung erfasst, wurden die implementierten Belastungs- und Erholungs-parameter an freiwilligen Testern/innen in der Praxis überprüft. Um sowohl die Applikation als auch die z.T. selbst entwickelten Kenngrößen auf ihre externe Validität hin zu bewerten, werden diese regressionsanalytisch bearbeitet.
Efficient and comprehensive assessment of students knowledge is an imperative task in any learning process. Short answer grading is one of the most successful methods in assessing the knowledge of students. Many supervised learning and deep learning approaches have been used to automate the task of short answer grading in the past. We investigate why assistive grading with active learning would be the next logical step in this task as there is no absolute ground truth answer for any question and the task is very subjective in nature. We present a fast and easy method to harness the power of active learning and natural language processing in assisting the task of grading short answer questions. A webbased GUI is designed and implemented to incorporate an interactive short answer grading system. The experiments show that active learning saves the time and effort of graders in assessment and reaches the performance of supervised learning with less amount of graded answers for training.
Trust is the lubricant of the sharing economy. This is true especially in peer-to-peer carsharing, in which one leaves a highly valuable good to a stranger in the hope of getting it back unscathed. Nowadays, ratings of other users are major mechanisms for establishing trust. To foster uptake of peer-to-peer carsharing, connected car technology opens new possibilities to support trust-building, e.g., by adding driving behavior statistics to users' profiles. However, collecting such data intrudes into rentees' privacy. To explore the tension between the need for trust and privacy demands, we conducted three focus group and eight individual interviews. Our results show that connected car technologies can increase trust for car owners and rentees not only before but also during and after rentals. The design of such systems must allow a differentiation between information in terms of type, the context, and the negotiability of information disclosure.
There has been increasing interest in designing for dementia in recent years. Empirical investigation is now needed of the long-term role of caregivers in appropriating ICTs into the complex daily life of people with dementia (PwD). We present here the outcomes of a 4-month evaluation of the individual, social and institutional impact of a videogame-based training system. The everyday behavior and interactions of 52 PwD and 25 caregivers was studied qualitatively, focusing on the role played by caregivers in integrating the system into daily routines. Our results indicate that the successful appropriation of ICT for PwD depends partly on the physical, cognitive and social benefits for PwD, but especially on the added value perceived by their social care-network. We discuss the need for design in dementia to develop more socially embedded innovations that can address the social actors involved and thus contribute to practical solutions for professional and private care.
When a robotic agent experiences a failure while acting in the world, it should be possible to discover why that failure has occurred, namely to diagnose the failure. In this paper, we argue that the diagnosability of robot actions, at least in a classical sense, is a feature that cannot be taken for granted since it strongly depends on the underlying action representation. We specifically define criteria that determine the diagnosability of robot actions. The diagnosability question is then analysed in the context of a handle manipulation action, such that we discuss two different representations of the action – a composite policy with a learned success model for the action parameters, and a neural network-based monolithic policy – both of which exist on different sides of the diagnosability spectrum. Through this comparison, we conclude that composite actions are more suited to explicit diagnosis, but representations with less prior knowledge are more flexible. This suggests that model learning may provide balance between flexibility and diagnosability; however, data-driven diagnosis methods also need to be enhanced in order to deal with the complexity of modern robots.
Risk-based Authentication (RBA) is an adaptive security measure to strengthen password-based authentication. RBA monitors additional features during login, and when observed feature values differ significantly from previously seen ones, users have to provide additional authentication factors such as a verification code. RBA has the potential to offer more usable authentication, but the usability and the security perceptions of RBA are not studied well.
We present the results of a between-group lab study (n=65) to evaluate usability and security perceptions of two RBA variants, one 2FA variant, and password-only authentication. Our study shows with significant results that RBA is considered to be more usable than the studied 2FA variants, while it is perceived as more secure than password-only authentication in general and comparably secure to 2FA in a variety of application types. We also observed RBA usability problems and provide recommendations for mitigation. Our contribution provides a first deeper understanding of the users' perception of RBA and helps to improve RBA implementations for a broader user acceptance.
Usability und User Experience (UX) haben als Design-Aspekte in der Produktentwicklung zunehmend an Bedeutung gewonnen. Daher ist es sinnvoll, die organisationale Kompetenz zur Entwicklung von Produkten mit einer positiven UX zu stärken. Veränderungen in Organisationen sind jedoch mit großem Aufwand verbunden. Deshalb müssen Organisationen entscheiden, welche Aktivitäten zur Veränderung der eigenen Kompetenz durchgeführt werden sollen und welche nicht. Die bisherige Forschung hat sich weitgehend auf die Anwendbarkeit bestimmter Methoden im Projekt- und Produktkontext konzentriert. Um geeignete Aktivitäten zur Verbesserung der organisationalen UX-Kompetenz zu identifizieren, wurden 17 UX-Professionals befragt. Diese UX-Professionals haben mindestens zehn Jahre Erfahrung durch die Arbeit in mehreren Unternehmen und durch die Übernahme einer Führungsrolle im Bereich UX gesammelt. Aus diesen Interviews wurden 13 mögliche Maßnahmen zur Steigerung der UX-Kompetenz von Organisationen abgeleitet. Dazu gehören beispielsweise die Erhöhung der Kompetenz einzelner Mitarbeiter, das Teilen von UX-Erfolgsgeschichten oder das Ermöglichen von User Research.
Long-term variability of solar irradiance and its implications for photovoltaic power in West Africa
(2020)
West Africa is one of the least developed regions in the world regarding the energy availability and energy security. Located close to the equator West Africa receives high amounts of global horizontal irradiance (GHI). Thus, solar power and especially photovoltaic (PV) systems seem to be a promising solution to provide electricity with low environmental impact. To plan and to dimension a PV power system climatological data for global horizontal irradiance (GHI) and its variability need to be taken into account. However, ground based measurements of irradiances are not available continuously and cover only a few discrete locations.
Compliant manipulation is a crucial skill for robots when they are supposed to act as helping hands in everyday household tasks. Still, nowadays, those skills are hand-crafted by experts which frequently requires labor-intensive, manual parameter tuning. Moreover, some tasks are too complex to be specified fully using a task specification. Learning these skills, by contrast, requires a high number of costly and potentially unsafe interactions with the environment. We present a compliant manipulation approach using reinforcement learning guided by the Task Frame Formalism, a task specification method. This allows us to specify the easy to model knowledge about a task while the robot learns the unmodeled components by reinforcement learning. We evaluate the approach by performing a compliant manipulation task with a KUKA LWR 4+ manipulator. The robot was able to learn force control policies directly on the robot without using any simulation.
Gone But Not Forgotten: Evaluating Performance and Scalability of Real-Time Mesoscopic Agents
(2020)
Telepresence robots allow people to participate in remote spaces, yet they can be difficult to manoeuvre with people and obstacles around. We designed a haptic-feedback system called “FeetBack," which users place their feet in when driving a telepresence robot. When the robot approaches people or obstacles, haptic proximity and collision feedback are provided on the respective sides of the feet, helping inform users about events that are hard to notice through the robot’s camera views. We conducted two studies: one to explore the usage of FeetBack in virtual environments, another focused on real environments.We found that FeetBack can increase spatial presence in simple virtual environments. Users valued the feedback to adjust their behaviour in both types of environments, though it was sometimes too frequent or unneeded for certain situations after a period of time. These results point to the value of foot-based haptic feedback for telepresence robot systems, while also the need to design context-sensitive haptic feedback.
This paper introduces FaceHaptics, a novel haptic display based on a robot arm attached to a head-mounted virtual reality display. It provides localized, multi-directional and movable haptic cues in the form of wind, warmth, moving and single-point touch events and water spray to dedicated parts of the face not covered by the head-mounted display.The easily extensible system, however, can principally mount any type of compact haptic actuator or object. User study 1 showed that users appreciate the directional resolution of cues, and can judge wind direction well, especially when they move their head and wind direction is adjusted dynamically to compensate for head rotations. Study 2 showed that adding FaceHaptics cues to a VR walkthrough can significantly improve user experience, presence, and emotional responses.
Exploring Future Work - Co-Designing a Human-robot Collaboration Environment for Service Domains
(2020)
There has been increasing interest in the application of humanoid robots in service domains like retail or care homes in recent years. Here, most use cases focus on serving customer needs autonomously. Frequently, human intervention becomes necessary to support the robot in exceptional situations. However, direct intervention of service operators is often not possible and requires specialized personnel. In a co-design process with 13 service operators from a pharmacy, we designed a remote working environment for human-robot collaboration that enables first-time experiences and collaboration with robots. Five participants took part in an assessment study and reported on their experiences about the utility, usability and user experience. Results show that participants were able to control and train the robot through the remote control environment. We discuss implications of our results for future work in service domains and emphasize a shift of focus from full robot automatization to human-robot collaboration forms.
In 1991 the researchers at the center for the Learning Sciences of Carnegie Mellon University were confronted with the confusing question of “where is AI” from the users, who were interacting with AI but did not realize it. Three decades of research and we are still facing the same issue with the AItechnology users. In the lack of users’ awareness and mutual understanding of AI-enabled systems between designers and users, informal theories of the users about how a system works (“Folk theories”) become inevitable but can lead to misconceptions and ineffective interactions. To shape appropriate mental models of AI-based systems, explainable AI has been suggested by AI practitioners. However, a profound understanding of the current users’ perception of AI is still missing. In this study, we introduce the term “Perceived AI” as “AI defined from the perspective of its users”. We then present our preliminary results from deep-interviews with 50 AItechnology users, which provide a framework for our future research approach towards a better understanding of PAI and users’ folk theories.
Risk-based Authentication (RBA) is an adaptive security measure that improves the security of password-based authentication by protecting against credential stuffing, password guessing, or phishing attacks. RBA monitors extra features during login and requests for an additional authentication step if the observed feature values deviate from the usual ones in the login history. In state-of-the-art RBA re-authentication deployments, users receive an email with a numerical code in its body, which must be entered on the online service. Although this procedure has a major impact on RBA's time exposure and usability, these aspects were not studied so far.
We introduce two RBA re-authentication variants supplementing the de facto standard with a link-based and another code-based approach. Then, we present the results of a between-group study (N=592) to evaluate these three approaches. Our observations show with significant results that there is potential to speed up the RBA re-authentication process without reducing neither its security properties nor its security perception. The link-based re-authentication via "magic links", however, makes users significantly more anxious than the code-based approaches when perceived for the first time. Our evaluations underline the fact that RBA re-authentication is not a uniform procedure. We summarize our findings and provide recommendations.
Evaluation of a Multi-Layer 2.5D display in comparison to conventional 3D stereoscopic glasses
(2020)
In this paper we propose and evaluate a custom-build projection-based multilayer 2.5D display, consisting of three layers of images, and compare performance to a stereoscopic 3D display. Stereoscopic vision can increase the involvement and enhance game experience, however may induce possible side effects, e.g. motion sickness and simulator sickness. To overcome the disadvantage of multiple discrete depths, in our system perspective rendering and head-tracking is used. A study was performed to evaluate this display with 20 participants playing custom-designed games. The results indicated that the multi-layer display caused fewer side effects than the stereoscopic display and provided good usability. The participants also stated a better or equal spatial perception, while the cognitive load stayed the same.
This paper presents groupware to study group behavior while conducting a creative task on large, high-resolution displays. Moreover, we present the results of a between-subjects study. In the study, 12 groups with two participants each prototyped a 2D level on a 7m x 2.5m large, high-resolution display using tablet-PCs for interaction. Six groups underwent a condition where group members had equal roles and interaction possibilities. Another six groups worked in a condition where group members had different roles: level designer and 2D artist. The results revealed that in the different roles condition, the participants worked significantly more tightly and created more assets. We could also detect some shortcomings for that configuration. We discuss the gained insights regarding system configuration, groupware interfaces, and groups behavior.
The encoding of solutions in black-box optimization is a delicate, handcrafted balance between expressiveness and domain knowledge between exploring a wide variety of solutions, and ensuring that those solutions are useful. Our main insight is that this process can be automated by generating a dataset of high-performing solutions with a quality diversity algorithm (here, MAP-Elites), then learning a representation with a generative model (here, a Varia-tional Autoencoder) from that dataset. Our second insight is that this representation can be used to scale quality diversity optimization to higher dimensions-but only if we carefully mix solutions generated with the learned representation and those generated with traditional variation operators. We demonstrate these capabilities by learning an low-dimensional encoding for the inverse kinemat-ics of a thousand joint planar arm. The results show that learned representations make it possible to solve high-dimensional problems with orders of magnitude fewer evaluations than the standard MAP-Elites, and that, once solved, the produced encoding can be used for rapid optimization of novel, but similar, tasks. The presented techniques not only scale up quality diversity algorithms to high dimensions, but show that black-box optimization encodings can be automatically learned, rather than hand designed.
Bei genauer Betrachtung heutiger Sharing Plattformen wie AirBnB, Uber, Drivy oder Fairleihen fällt auf, dass diese eines gemein haben. Als Plattformökonomien basieren sie auf mindestens zwei Nutzergruppen, Anbietern und Nachfragern für Güter oder Dienstleistungen. Ein Problem solcher zweioder mehrseitigen Märkte ist jedoch häufig, dass der Wertezuwachs, der durch die Nutzer generiert wird, nicht gleichmäßig unter der Plattform und den aktiven Nutzern verteilt wird, sondern meist ausschließlich als Gewinn an die Plattformen geht. Mit der Blockchain-Technologie könnte dieses Problem gelöst werden, indem der Informations- und Wertetransfer sicher und dezentral organisiert wird und viele Funktionen traditioneller Intermediäre dadurch obsolet werden. Diese Arbeit bietet einen Überblick über Anwendungsfelder und das Grundkonzept der Sharing Economy. Wir zeigen auf, wie sich Geschäftsmodelle und Infrastrukturen in einer Blockchain abbilden lassen, welche Potentiale eine Blockchain-basierte Infrastruktur bietet, wann diese in der Sharing Economy sinnvoll sein kann und welche Probleme dadurch gelöst werden können.
This work presents the development of a measuring system for the quality control of ultrapure water. The new systems combines ozonation and UV radiation for the oxidation of organic substances. The change in conductivity caused by the oxidation is furthermore correlated with the TOC of the solution.