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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.
With economic weight shifting toward net zero, now is the time for ECAs, Exim-Banks, and PRIs to lead. Despite previous success, aligning global economic governance to climate goals requires additional activities across export finance and investment insurance institutions. The new research project initiated by Oxford University, ClimateWorks Foundation, and Mission 2020 including other practitioners and academics from institutions such as Atradius DSB, Columbia University, EDC, FMO and Offenburg University focuses on reshaping future trade and investment governance in light of climate action. The idea of a ‘Berne Union Net Zero Club’ is an important item in a potential package of reforms. This can include realigning mandates and corporate strategies, principles of intervention, as well as ECA, Exim-Bank and PRI operating models in order to accelerate net zero transformation. Full transparency regarding Berne Union members’ activities would be an excellent starting point. We invite all interested parties in the sector to come together to chart our own path to net zero
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.
A method for 3D printing of a robot element, more particularly a finger for use in robotics. At least one sensor is concomitantly printed by means of multi-material printing during the printing of the robot element. A gripping element produced by a method of this kind includes a number of printed layers of robot element material and a concomitantly printed sensor.
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).
Multiple Object Tracking (MOT) is a long-standing task in computer vision. Current approaches based on the tracking by detection paradigm either require some sort of domain knowledge or supervision to associate data correctly into tracks. In this work, we present an unsupervised multiple object tracking approach based on visual features and minimum cost lifted multicuts. Our method is based on straight-forward spatio-temporal cues that can be extracted from neighboring frames in an image sequences without superivison. Clustering based on these cues enables us to learn the required appearance invariances for the tracking task at hand and train an autoencoder to generate suitable latent representation. Thus, the resulting latent representations can serve as robust appearance cues for tracking even over large temporal distances where no reliable spatio-temporal features could be extracted. We show that, despite being trained without using the provided annotations, our model provides competitive results on the challenging MOT Benchmark for pedestrian tracking.
Time-of-Flight Cameras Enabling Collaborative Robots for Improved Safety in Medical Applications
(2020)
Human-robot collaboration is being used more and more in industry applications and is finding its way into medical applications. Industrial robots that are used for human-robot collaboration, cannot detect obstacles from a distance. This paper introduced the idea of using wireless technology to connect a Time-of-Flight camera to off-the-shelf industrial robots. This way, the robot can detect obstacles up to a distance of five meters. Connecting Time-of-Flight cameras to robots increases the safety in human-robot collaboration by detecting obstacles before a collision. After looking at the state of the art, the authors elaborated the different requirements for such a system. The Time-of-Flight camera from Heptagon is able to work in a range of up to five meters and can connect to the control unit of the robot via a wireless connection.
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.
Silicon (Si) has turned out to be a promising active material for next‐generation lithium‐ion battery anodes. Nevertheless, the issues known from Si as electrode material (pulverization effects, volume change etc.) are impeding the development of Si anodes to reach market maturity. In this study, we are investigating a possible application of Si anodes in low‐power printed electronic applications. Tailored Si inks are produced and the impact of carbon coating on the printability and their electrochemical behavior as printed Si anodes is investigated. The printed Si anodes contain active material loadings that are practical for powering printed electronic devices, like electrolyte gated transistors, and are able to show high capacity retentions. A capacity of 1754 mAh/gSi is achieved for a printed Si anode after 100 cycles. Additionally, the direct applicability of the printed Si anodes is shown by successfully powering an ink‐jet printed transistor.
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.