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Now showing 1 - 7 of 7
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    Like a Second Skin: Understanding How Epidermal Devices Affect Human Tactile Perception
    (New York,NY,United States : Association for Computing Machinery, 2019) Nittala, Aditya Shekhar; Kruttwig, Klaus; Lee, Jaeyeon; Bennewitz, Roland; Arzt, Eduard; Steimle, Jürgen; Brewster, Stephen
    The emerging class of epidermal devices opens up new opportunities for skin-based sensing, computing, and interaction. Future design of these devices requires an understanding of how skin-worn devices affect the natural tactile perception. In this study, we approach this research challenge by proposing a novel classification system for epidermal devices based on flexural rigidity and by testing advanced adhesive materials, including tattoo paper and thin films of poly (dimethylsiloxane) (PDMS). We report on the results of three psychophysical experiments that investigated the effect of epidermal devices of different rigidity on passive and active tactile perception. We analyzed human tactile sensitivity thresholds, two-point discrimination thresholds, and roughness discrimination abilities on three different body locations (fingertip, hand, forearm). Generally, a correlation was found between device rigidity and tactile sensitivity thresholds as well as roughness discrimination ability. Surprisingly, thin epidermal devices based on PDMS with a hundred times the rigidity of commonly used tattoo paper resulted in comparable levels of tactile acuity. The material offers the benefit of increased robustness against wear and the option to re-use the device. Based on our findings, we derive design recommendations for epidermal devices that combine tactile perception with device robustness.
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    PhysioSkin: Rapid Fabrication of Skin-Conformal Physiological Interfaces
    (New York,NY,United States : Association for Computing Machinery, 2020) Nittala, Aditya Shekhar; Khan, Arshad; Kruttwig, Klaus; Kraus, Tobias; Steimle, Jürgen; Bernhaupt, Regina
    Advances in rapid prototyping platforms have made physiological sensing accessible to a wide audience. However, off-the-shelf electrodes commonly used for capturing biosignals are typically thick, non-conformal and do not support customization. We present PhysioSkin, a rapid, do-it-yourself prototyping method for fabricating custom multi-modal physiological sensors, using commercial materials and a commodity desktop inkjet printer. It realizes ultrathin skin-conformal patches (~1μm) and interactive textiles that capture sEMG, EDA and ECG signals. It further supports fabricating devices with custom levels of thickness and stretchability. We present detailed fabrication explorations on multiple substrate materials, functional inks and skin adhesive materials. Informed from the literature, we also provide design recommendations for each of the modalities. Evaluation results show that the sensor patches achieve a high signal-to-noise ratio. Example applications demonstrate the functionality and versatility of our approach for prototyping a next generation of physiological devices that intimately couple with the human body.
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    Metadata analysis of open educational resources
    (New York,NY,United States : Association for Computing Machinery, 2021) Tavakoli, Mohammadreza; Elias, Mirette; Kismihók, Gábor; Auer, Sören; Scheffel, Maren
    Open Educational Resources (OERs) are openly licensed educational materials that are widely used for learning. Nowadays, many online learning repositories provide millions of OERs. Therefore, it is exceedingly difficult for learners to find the most appropriate OER among these resources. Subsequently, the precise OER metadata is critical for providing high-quality services such as search and recommendation. Moreover, metadata facilitates the process of automatic OER quality control as the continuously increasing number of OERs makes manual quality control extremely difficult. This work uses the metadata of 8,887 OERs to perform an exploratory data analysis on OER metadata. Accordingly, this work proposes metadata-based scoring and prediction models to anticipate the quality of OERs. Based on the results, our analysis demonstrated that OER metadata and OER content qualities are closely related, as we could detect high-quality OERs with an accuracy of 94.6%. Our model was also evaluated on 884 educational videos from Youtube to show its applicability on other educational repositories.
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    Towards Analyzing the Bias of News Recommender Systems Using Sentiment and Stance Detection
    (New York,NY,United States : Association for Computing Machinery, 2022) Alam, Mehwish; Iana, Andreea; Grote, Alexander; Ludwig, Katharina; Müller, Philipp; Paulheim, Heiko; Laforest, Frédérique; Troncy, Raphael; Médini, Lionel; Herman, Ivan
    News recommender systems are used by online news providers to alleviate information overload and to provide personalized content to users. However, algorithmic news curation has been hypothesized to create filter bubbles and to intensify users' selective exposure, potentially increasing their vulnerability to polarized opinions and fake news. In this paper, we show how information on news items' stance and sentiment can be utilized to analyze and quantify the extent to which recommender systems suffer from biases. To that end, we have annotated a German news corpus on the topic of migration using stance detection and sentiment analysis. In an experimental evaluation with four different recommender systems, our results show a slight tendency of all four models for recommending articles with negative sentiments and stances against the topic of refugees and migration. Moreover, we observed a positive correlation between the sentiment and stance bias of the text-based recommenders and the preexisting user bias, which indicates that these systems amplify users' opinions and decrease the diversity of recommended news. The knowledge-aware model appears to be the least prone to such biases, at the cost of predictive accuracy.
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    Advancing environmental intelligence through novel approaches in soft bioinspired robotics and allied technologies: I-Seed project position paper for Environmental Intelligence in Europe
    (New York,NY,United States : Association for Computing Machinery, 2022) Mazzolai, Barbara; Kraus, Tobias; Pirrone, Nicola; Kooistra, Lammert; De Simone, Antonio; Cottin, Antoine; Margheri, Laura
    The EU-funded FET Proactive Environmental Intelligence project "I-Seed"(Grant Agreement n. 101017940, targets towards the development of a radically simplified and environmentally friendly approach for environmental monitoring. Specifically, I-Seed aims at developing a new generation of self-deployable and biodegradable soft miniaturized robots, inspired by the morphology and dispersion abilities of plant seeds, able to perform low-cost, environmentally responsible, in-situ measurements. The natural functional mechanisms of seeds dispersal offer a rich source of robust, highly adaptive, mass and energy efficient mechanisms, and behavioral and morphological intelligence, which can be selected and implemented for advanced, but simple, technological inventions. I-Seed robots are conceived as unique in their movement abilities because inspired by passive mechanisms and materials of natural seeds, and unique in their environmentally friendly design because made of all biodegradable components. Sensing is based on a chemical transduction mechanism in a stimulus-responsive sensor material with fluorescence-based optical readout, which can be read via one or more drones equipped with fluorescent LiDAR technology and a software able to perform a real time georeferencing of data. The I-Seed robotic ecosystem is envisioned to be used for collecting environmental data in-situ with high spatial and temporal resolution across large remote areas where no monitoring data are available, and thus for extending current environmental sensor frameworks and data analysis systems.
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    IWILDS'22 - Third International Workshop on Investigating Learning During Web Search
    (New York,NY,United States : Association for Computing Machinery, 2022) Hoppe, Anett; Yu, Ran; Liu, Jiqun; Amigo, Enrique
    Since its inception, the World Wide Web has become a major information source, consulted for a diversity of informational tasks. With an abundance of information available online, Web search engines have been a main entry point, supporting users in finding suitable Web content for ever more complex information needs. The IWILDS workshop series invites research on complex search activities related to human learning. It provides an interdisciplinary platform for the presentation and discussion of recent research on human learning on the Web, welcoming perspectives from computer & information science, education and psychology.
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    Collaborative annotation and semantic enrichment of 3D media
    (New York,NY,United States : Association for Computing Machinery, 2022) Rossenova, Lozana; Schubert, Zoe; Vock, Richard; Sohmen, Lucia; Günther, Lukas; Duchesne, Paul; Blümel, Ina; Aizawa, Akiko
    A new FOSS (free and open source software) toolchain and associated workflow is being developed in the context of NFDI4Culture, a German consortium of research- and cultural heritage institutions working towards a shared infrastructure for research data that meets the needs of 21st century data creators, maintainers and end users across the broad spectrum of the digital libraries and archives field, and the digital humanities. This short paper and demo present how the integrated toolchain connects: 1) OpenRefine - for data reconciliation and batch upload; 2) Wikibase - for linked open data (LOD) storage; and 3) Kompakkt - for rendering and annotating 3D models. The presentation is aimed at librarians, digital curators and data managers interested in learning how to manage research datasets containing 3D media, and how to make them available within an open data environment with 3D-rendering and collaborative annotation features.