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Wow, You Are Terrible at This!: An Intercultural Study on Virtual Agents Giving Mixed Feedback
(2020)
While the effects of virtual agents in terms of likeability, uncanniness, etc. are well explored, it is unclear how their appearance and the feedback they give affects people's reactions. Is critical feedback from an agent embodied as a mouse or a robot taken less serious than from a human agent? In an intercultural study with 120 participants from Germany and the US, participants had to find hidden objects in a game and received feedback on their performance by virtual agents with different appearances. As some levels were designed to be unsolvable, critical feedback was unavoidable. We hypothesized that feedback would be taken more serious, the more human the agent looked. Also, we expected the subjects from the US to react more sensitively to criticism. Surprisingly, our results showed that the agents' appearance did not significantly change the participants' perception. Also, while we found highly significant differences in inspirational and motivational effects as well as in perceived task load between the two cultures, the reactions to criticism were contrary to expectations based on established cultural models. This work improves our understanding on how affective virtual agents are to be designed, both with respect to culture and to dialogue strategies.
Additive manufacturing (AM) or 3D printing (3DP) has become a widespread new technology in recent years and is now used in many areas of industry. At the same time, there is an increasing need for training courses that impart the knowledge required for product development in 3D printing. In this article, a workshop on “Rapid Prototyping” is presented, which is intended to provide students with the technical and creative knowledge for product development in the field of AM. Today, additive manufacturing is an important part of teaching for the training of future engineers. In a detailed literature review, the advantages and disadvantages of previous approaches to training students are examined and analyzed. On this basis, a new approach is developed in which the students analyze and optimize a given product in terms of additivie manufacturing. The students use two different 3D printers to complete this task. In this way, the students acquire the skills to work independently with different processes and materials. With this new approach, the students learn to adapt the design to different manufacturing processes and to observe the restrictions of different materials. The results of these courses are evaluated through feedback in a presentation and a questionnaire.
This paper describes a comparative study of two tactile systems supporting navigation for persons with little or no visual and auditory perception. The efficacy of a tactile head-mounted device (HMD) was compared to that of a wearable device, a tactile belt. A study with twenty participants showed that the participants took significantly less time to complete a course when navigating with the HMD, as compared to the belt.
Generative convolutional deep neural networks, e.g. popular GAN architectures, are relying on convolution based up-sampling methods to produce non-scalar outputs like images or video sequences. In this paper, we show that common up-sampling methods, i.e. known as up-convolution or transposed convolution, are causing the inability of such models to reproduce spectral distributions of natural training data correctly. This effect is independent of the underlying architecture and we show that it can be used to easily detect generated data like deepfakes with up to 100% accuracy on public benchmarks. To overcome this drawback of current generative models, we propose to add a novel spectral regularization term to the training optimization objective. We show that this approach not only allows to train spectral consistent GANs that are avoiding high frequency errors. Also, we show that a correct approximation of the frequency spectrum has positive effects on the training stability and output quality of generative networks.
Mit der Implementierung sowie einer anschließenden aussagekräftigen Evaluierung, soll das, visuelle-inertiale Kartierungs- und Lokalisierungssystem maplab analysiert werden. Hierbei basiert die Kartierung bzw. Lokalisierung auf der Detektion von Umgebungsmerkmalen. Neben der Möglichkeit der Kartenerstellung besteht ferner die Option, mehrere Karten zu fusionieren und somit weitreichende Gebiete zu kartieren sowie für weitere Datenauswertungen zu nutzen. Aufgrund der Durchführung und Bewertung der Ergebnisse in unterschiedlichen Anwendungsszenarien zeigt sich, dass maplab besonders zur Kartierung von Räumen bzw. kleinen Gebäudekomplexen geeignet ist. Die Möglichkeit der Kartenfusionierung bietet weiterhin die Option, den Informationsgehalt von Karten zu erhöhen, welches die Effektivität für eine anschließende Lokalisierung steigert. Bei wachsender Kartierungsgröße hingegen zeigt sich jedoch eine Vergrößerung geometrischer Inkonsistenzen.
Konstrukteure im Maschinenbau stehen häufig vor der Problemstellung, hochfest vorgespannte Schraubenverbindungen und einen durchgehenden Korrosionsschutz zu vereinen. Die Normen und Richtlinien bieten hierzu Stand heute keine ausreichenden Antworten. Die Hochschule Offenburg befasst sich im Rahmen einer industriellen Gemeinschaftsforschung mit der Fragestellung, welchen Einfluss organische Beschichtungen auf die Vorspannkraft insbesondere bei erhöhten Umgebungstemperaturen haben. In dieser Arbeit werden die ersten Ergebnisse zum Einfluss der Einzelschichtstärke des Beschichtungssystems präsentiert.
To reach customers by dialog marketing campaigns is more and more difficult. This is a common problem of companies and marketing agencies worldwide: information overload, multi-channel-communication and a confusing variety of offers make it hard to gain the attention of the target group. The contribution of this paper is four-fold: we provide an overview of the current state of print dialog marketing activities and trends (I). Based on this corpus we identify the main key performance indicators of dialog marketing customer interaction (II). A qualitative user experience study identifies the customer wishes and needs, focusing on lottery offers for senior citizens (III). Finally, we evaluate the success of two different dialog marketing campaigns with 20,000 clients and compare the key performance indicators of the original hands-on experience-based print mailings with user experience tested and optimized mailings (IV).
One of the main requirements of spatially distributed Internet of Things (IoT) solutions is to have networks with wider coverage to connect many low-power devices. Low-Power Wide-Area Networks (LPWAN) and Cellular IoT(cIOT) networks are promising candidates in this space. LPWAN approaches are based on enhanced physical layer (PHY) implementations to achieve long range such as LoRaWAN, SigFox, MIOTY. Narrowband versions of cellular network offer reduced bandwidth and, simplified node and network management mechanisms, such as Narrow Band IoT (NB-IoT) and Long-Term Evolution for Machines (LTE-M). Since the underlying use cases come with various requirements it is essential to perform a comparative analysis of competing technologies. This article provides systematic performance measurement and comparison of LPWAN and NB-IoT technologies in a unified testbed, also discusses the necessity of future fifth generation (5G) LPWAN solutions.
Machine learning (ML) has become highly relevant in applications across all industries, and specialists in the field are sought urgently. As it is a highly interdisciplinary field, requiring knowledge in computer science, statistics and the relevant application domain, experts are hard to find. Large corporations can sweep the job market by offering high salaries, which makes the situation for small and medium enterprises (SME) even worse, as they usually lack the capacities both for attracting specialists and for qualifying their own personnel. In order to meet the enormous demand in ML specialists, universities now teach ML in specifically designed degree programs as well as within established programs in science and engineering. While the teaching almost always uses practical examples, these are somewhat artificial or outdated, as real data from real companies is usually not available. The approach reported in this contribution aims to tackle the above challenges in an integrated course, combining three independent aspects: first, teaching key ML concepts to graduate students from a variety of existing degree programs; second, qualifying working professionals from SME for ML; and third, applying ML to real-world problems faced by those SME. The course was carried out in two trial periods within a government-funded project at a university of applied sciences in south-west Germany. The region is dominated by SME many of which are world leaders in their industries. Participants were students from different graduate programs as well as working professionals from several SME based in the region. The first phase of the course (one semester) consists of the fundamental concepts of ML, such as exploratory data analysis, regression, classification, clustering, and deep learning. In this phase, student participants and working professionals were taught in separate tracks. Students attended regular classes and lab sessions (but were also given access to e-learning materials), whereas the professionals learned exclusively in a flipped classroom scenario: they were given access to e-learning units (video lectures and accompanying quizzes) for preparation, while face-to-face sessions were dominated by lab experiments applying the concepts. Prior to the start of the second phase, participating companies were invited to submit real-world problems that they wanted to solve with the help of ML. The second phase consisted of practical ML projects, each tackling one of the problems and worked on by a mixed team of both students and professionals for the period of one semester. The teams were self-organized in the ways they preferred to work (e.g. remote vs. face-to-face collaboration), but also coached by one of the teaching staff. In several plenary meetings, the teams reported on their status as well as challenges and solutions. In both periods, the course was monitored and extensive surveys were carried out. We report on the findings as well as the lessons learned. For instance, while the program was very well-received, professional participants wished for more detailed coverage of theoretical concepts. A challenge faced by several teams during the second phase was a dropout of student members due to upcoming exams in other subjects.
Tactile Navigation with Checkpoints as Progress Indicators?: Only when Walking Longer Straight Paths
(2020)
Persons with both vision and hearing impairments have to rely primarily on tactile feedback, which is frequently used in assistive devices. We explore the use of checkpoints as a way to give them feedback during navigation tasks. Particularly, we investigate how checkpoints can impact performance and user experience. We hypothesized that individuals receiving checkpoint feedback would take less time and perceive the navigation experience as superior to those who did not receive such feedback. Our contribution is two-fold: a detailed report on the implementation of a smart wearable with tactile feedback (1), and a user study analyzing its effects (2). The results show that in contrast to our assumptions, individuals took considerably more time to complete routes with checkpoints. Also, they perceived navigating with checkpoints as inferior to navigating without checkpoints. While the quantitative data leave little room for doubt, the qualitative data open new aspects: when walking straight and not being "overwhelmed" by various forms of feedback in succession, several participants actually appreciated the checkpoint feedback.
Deafblindness, also known as dual sensory loss, is the combination of sight and hearing impairments of such extent that it becomes difficult for one sense to compensate for the other. Communication issues are a key concern for the Deafblind community. We present the design and technical implementation of the Tactile Board: a mobile Augmentative and Alternative Communication (AAC) device for individuals with deafblindness. The Tactile Board allows text and speech to be translated into vibrotactile signs that are displayed real-time to the user via a haptic wearable. Our aim is to facilitate communication for the deafblind community, creating opportunities for these individuals to initiate and engage in social interactions with other people without the direct need of an intervener.
This book constitutes the refereed proceedings of the 20th International TRIZ Future Conference, TFC 2020, held online at the University Cluj-Napoca, Romania, in October 2020 and sponsored by the International Federation for Information Processing.
34 chapters were carefully peer reviewed and selected from 91 conference submissions. They are organized in the following thematic sections: computing TRIZ; education and pedagogy; sustainable development; tools and techniques of TRIZ for enhancing design; TRIZ and system engineering; TRIZ and complexity; and cross-fertilization of TRIZ for innovation management.
A novel approach for synchronization and calibration of a camera and an inertial measurement unit (IMU) in the research-oriented visual-inertial mapping-and localization-framework maplab is presented. Mapping and localization are based on detecting different features in the environment. In addition to the possibility of creating single-case maps, the included algorithms allow merging maps to increase mapping accuracy and obtain large-scale maps. Furthermore, the algorithms can be used to optimize the collected data. The preliminary results show that after appropriate calibration and synchronization maplab can be used efficiently for mapping, especially in rooms and small building environments.
Diffracted waves carry high resolution information that can help interpreting fine structural details at a scale smaller than the seismic wavelength. Because of the low signal-to-noise ratio of diffracted waves, it is challenging to preserve them during processing and to identify them in the final data. It is, therefore, a traditional approach to pick manually the diffractions. However, such task is tedious and often prohibitive, thus, current attention is given to domain adaptation. Those methods aim to transfer knowledge from a labeled domain to train the model, and then infer on the real unlabeled data. In this regard, it is common practice to create a synthetic labeled training dataset, followed by testing on unlabeled real data. Unfortunately, such procedure may fail due to the existing gap between the synthetic and the real distribution since quite often synthetic data oversimplifies the problem, and consequently the transfer learning becomes a hard and non-trivial procedure. Furthermore, deep neural networks are characterized by their high sensitivity towards cross-domain distribution shift. In this work, we present deep learning model that builds a bridge between both distributions creating a semi-synthetic datatset that fills in the gap between synthetic and real domains. More specifically, our proposal is a feed-forward, fully convolutional neural network for imageto-image translation that allows to insert synthetic diffractions while preserving the original reflection signal. A series of experiments validate that our approach produces convincing seismic data containing the desired synthetic diffractions.
Strings
(2020)
This article presents the currently ongoing development of an audiovisual performance work with the title Strings. This work provides an improvisation setting for a violinist, two laptop performers, and two generative systems. At the core of Strings lies an approach that establishes a strong correlation among all participants by means of a shared physical principle. The physical principle is that of a vibrating string. The article discusses how this principle is used in both natural and simulated forms as main interaction layer between all performers and as natural or generative principle for creating audio and video.
We propose in this work to solve privacy preserving set relations performed by a third party in an outsourced configuration. We argue that solving the disjointness relation based on Bloom filters is a new contribution in particular by having another layer of privacy on the sets cardinality. We propose to compose the set relations in a slightly different way by applying a keyed hash function. Besides discussing the correctness of the set relations, we analyze how this impacts the privacy of the sets content as well as providing privacy on the sets cardinality. We are in particular interested in how having bits overlapping in the Bloom filters impacts the privacy level of our approach. Finally, we present our results with real-world parameters in two concrete scenarios.
Due to the rapidly increasing storage consumption worldwide, as well as the expectation of continuous availability of information, the complexity of administration in today’s data centers is growing permanently. Integrated techniques for monitoring hard disks can increase the reliability of storage systems. However, these techniques often lack intelligent data analysis to perform predictive maintenance. To solve this problem, machine learning algorithms can be used to detect potential failures in advance and prevent them. In this paper, an unsupervised model for predicting hard disk failures based on Isolation Forest is proposed. Consequently, a method is presented that can deal with the highly imbalanced datasets, as the experiment on the Backblaze benchmark dataset demonstrates.