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End date: 2019 × Start date: 2019 × Subject: Konferenzschrift ×

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    EVENTSKG: A 5-Star Dataset of Top-Ranked Events in Eight Computer Science Communities
    (Berlin ; Heidelberg : Springer, 2019) Fathalla, Said; Lange, Christoph; Auer, Sören; Hitzler, Pascal; Fernández, Miriam; Janowicz, Krzysztof; Zaveri, Amrapali; Gray, Alasdair J.G.; Lopez, Vanessa; Haller, Armin; Hammar, Karl
    Metadata of scientific events has become increasingly available on the Web, albeit often as raw data in various formats, disregarding its semantics and interlinking relations. This leads to restricting the usability of this data for, e.g., subsequent analyses and reasoning. Therefore, there is a pressing need to represent this data in a semantic representation, i.e., Linked Data. We present the new release of the EVENTSKG dataset, comprising comprehensive semantic descriptions of scientific events of eight computer science communities. Currently, EVENTSKG is a 5-star dataset containing metadata of 73 top-ranked event series (almost 2,000 events) established over the last five decades. The new release is a Linked Open Dataset adhering to an updated version of the Scientific Events Ontology, a reference ontology for event metadata representation, leading to richer and cleaner data. To facilitate the maintenance of EVENTSKG and to ensure its sustainability, EVENTSKG is coupled with a Java API that enables users to add/update events metadata without going into the details of the representation of the dataset. We shed light on events characteristics by analyzing EVENTSKG data, which provides a flexible means for customization in order to better understand the characteristics of renowned CS events.
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    The Research Core Dataset (KDSF) in the Linked Data context
    (Amsterdam [u.a.] : Elsevier, 2019) Walther, Tatiana; Hauschke, Christian; Kasprzik, Anna; Sicilia, Miguel-Angel; Simons, Ed; Clements, Anna; de Castro, Pablo; Bergström, Johan
    This paper describes our efforts to implement the Research Core Dataset (“Kerndatensatz Forschung”; KDSF) as an ontology in VIVO. KDSF is used in VIVO to record the required metadata on incoming data and to produce reports as an output. While both processes need an elaborate adaptation of the KDSF specification, this paper focusses on the adaptation of the KDSF basic data model for recording data in VIVO. In this context, the VIVO and KDSF ontologies were compared with respect to domain, syntax, structure, and granularity in order to identify correspondences and mismatches. To produce an alignment, different matching approaches have been applied. Furthermore, we made necessary modifications and extensions on KDSF classes and properties.
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    Las Pailas geothermal field - Central America case study: Deciphering a volcanic geothermal play type through the combination of optimized geophysical exploration methods and classic geological conceptual models of volcano-tectonic systems
    (London [u.a.] : Institute of Physics, 2019) Salguero, Leonardo Solís; Rioseco, Ernesto Meneses
    Sustainable exploitation strategies of high-enthalpy geothermal reservoirs in a volcanic geothermal play type require an accurate understanding of key geological structures such as faults, cap rock and caldera boundaries. Of same importance is the recognition of possible magmatic body intrusions and their morphology, whether they are tabular like dikes, layered like sills or domes. The relative value of those magmatic bodies, their age, shape and location rely on the role they play as possible local heat sources, hydraulic barriers between reservoir compartments, and their far-reaching effect on the geochemistry and dynamics of fluids. Obtaining detailed knowledge and a more complete understanding at the early stages of exploration through integrated geological, geophysical and geochemical methods is essential to determine promising geothermal drilling targets for optimized production/re-injection schemes and for the development of adequate exploitation programs. Valuable, extensive geophysical data gathered at Las Pailas high-enthalpy geothermal field at northwestern Costa Rica combined with detailed understanding of the geological structures in the underground may represent a sound basis for an in-depth geoscientific discussion on this topic. Currently, the German cooperation for the identification of geothermal resources in Central America, implemented by the Federal Institute for Geosciences and Natural Resources (BGR), supports an international and interdisciplinary effort, driven by the Instituto Costarricense de Electricidad (ICE) with different international and national research institutions, including the Leibniz Institute for Applied Geophysics (LIAG). The discussions and joint studies refer to the optimized utilization of geophysical and geological methods for geothermal exploration in the Central American region, using the example of Las Pailas Geothermal Field. The results should contribute to a better understanding of the most appropriate geothermal exploration concepts for complex volcanic field settings in Central America.
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    Melt mixed composites of polypropylene with singlewalled carbon nanotubes for thermoelectric applications: Switching from p- to n-type behavior by additive addition
    (Melville, NY : AIP, 2019) Pötschke; Petra; Krause, Beate; Luo, Jinji
    Composites were prepared with polypropylene (PP) as the matrix and singlewalled CNTs (SWCNTs) of the type TUBALL from OCSiAl Ltd. as the conducting component by melt processing in a small-scale twin-screw compounder. In order to switch the typical p-type behavior of such composites from positive Seebeck coefficients (S) into n-type behavior with negative Seebeck coefficients, a non-ionic surfactant polyoxyethylene 20 cetyl ether (Brij58) was used and compared with a PEG additive, which was shown previously to be able to induce such switching. For PP-2 wt% SWCNT composites Brij58 is shown to result in n-type composites. The negative S values (up to −48.2 µV/K) are not as high as in the case of previous results using PEG (−56.6 µV/K). However, due to the more pronounced effect of Brij58 on the electrical conductivity, the achieved power factors are higher and reach a maximum of 0.144 µW/(m·K2) compared to previous 0.078 µW/(m·K2) with PEG. Dispersion improvement depends on the type of SWCNTs obtained by using varied synthesis/treatment conditions. Solution prepared composites of PEG with SWCNTs also have negative S values, indicating the donation of electrons from PEG to the SWCNTs. However, such composites are brittle and not suitable as thermoelectric materials.
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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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    Conversion of carbon dioxide into storable solar fuels using solar energy
    (London [u.a.] : Institute of Physics, 2019) Ennaceri, Houda; Abel, Bernd
    Nowadays, there are two main energy and environmental concerns, the first is the risk of running out of fossil fuels in the next few decades, and the second is the alarming increase in the carbon dioxide concentrations in the atmosphere, causing global warming and rise of see levels. Therefore, solar-driven technologies represent a substantial solution to fossil fuels dependence, global warming and climate change. Unlike most scientific research, which aim to use solar energy to generate electricity, solar energy can also be harnessed by recycling the carbon dioxide in the atmosphere through high-tech artificial photosynthesis with the objective of producing storable and liquid solar fuels from CO2 and water. There are two types of solar fuels, the first being hydrogen, which can be produced by mean of water splitting processes. The combustion of hydrogen generates water, which is a completely clean option for the environment. The second type of solar fuels consists of carbon-based fuels, such as methane (CH4), carbon monoxide (CO), or alcohols such as methanol (CH3OH) and ethanol (C2H5OH). The production to liquid solar fuels liquid fuels is of great interest, since they can be used in the current industrial infrastructures such as the automobiles' sector, without substantial changes in the vehicles' internal combustion engines. Therefore, guaranteeing a smooth transition from fossil fuel energy to renewable energy without radical economic consequences. Also, and most importantly, when these solar fuels are burned, they will only release the exact amount of CO2 which was initially used, which represents an optimal process for sustainable transport.
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    Semantic segmentation of non-linear multimodal images for disease grading of inflammatory bowel disease: A segnet-based application
    ([Sétubal] : SCITEPRESS - Science and Technology Publications Lda., 2019) Pradhan, Pranita; Meyer, Tobias; Vieth, Michael; Stallmach, Andreas; Waldner, Maximilian; Schmitt, Michael; Popp, Juergen; Bocklitz, Thomas; De Marsico, Maria; Sanniti di Baja, Gabriella; Fred, Ana
    Non-linear multimodal imaging, the combination of coherent anti-stokes Raman scattering (CARS), two-photon excited fluorescence (TPEF) and second harmonic generation (SHG), has shown its potential to assist the diagnosis of different inflammatory bowel diseases (IBDs). This label-free imaging technique can support the ‘gold-standard’ techniques such as colonoscopy and histopathology to ensure an IBD diagnosis in clinical environment. Moreover, non-linear multimodal imaging can measure biomolecular changes in different tissue regions such as crypt and mucosa region, which serve as a predictive marker for IBD severity. To achieve a real-time assessment of IBD severity, an automatic segmentation of the crypt and mucosa regions is needed. In this paper, we semantically segment the crypt and mucosa region using a deep neural network. We utilized the SegNet architecture (Badrinarayanan et al., 2015) and compared its results with a classical machine learning approach. Our trained SegNet mod el achieved an overall F1 score of 0.75. This model outperformed the classical machine learning approach for the segmentation of the crypt and mucosa region in our study.
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    Influence of a supplemental filler in twin-screw extruded PP/CNT composites using masterbatch dilution
    (Melville, NY : AIP, 2019) Müller, Michael Thomas; Krause, Beate; Kretzschmar, Bernd; Pötschke, Petra
    In this study commercially available multiwalled carbon nanotubes (2-8 wt.%) were incorporated in polypropylene (PP) by direct powder feeding or by a masterbatch dilution procedure using a twin-screw extruder. The influence of a supplemental, electrical non-conductive talc or electrically conductive carbon black (CB), filler on the resulting composite properties was investigated. In comparison to the direct carbon nanotube (CNT) incorporation the masterbatch dilution step resulted in improved CNT macro dispersion. The use of the supplemental fillers CB or talc does not show a significant influence on the CNT dispersion state. When compared to direct CNT incorporation, the second compounding process involved in masterbatch dilution leads to higher electrical resistivity of injection molded samples. On the other hand, the supplemental fillers talc or CB decreased the electrical resistivity values. With the addition of talc or CB an increase of the Young’s modulus due to the reinforcing effect of the second filler was achieved. However, no synergistic effect between the used supplemental fillers and the CNT on the mechanical properties was obtained.
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    Interaction Network Analysis Using Semantic Similarity Based on Translation Embeddings
    (Berlin ; Heidelberg : Springer, 2019) Manzoor Bajwa, Awais; Collarana, Diego; Vidal, Maria-Esther; Acosta, Maribel; Cudré-Mauroux, Philippe; Maleshkova, Maria; Pellegrini, Tassilo; Sack, Harald; Sure-Vetter, York
    Biomedical knowledge graphs such as STITCH, SIDER, and Drugbank provide the basis for the discovery of associations between biomedical entities, e.g., interactions between drugs and targets. Link prediction is a paramount task and represents a building block for supporting knowledge discovery. Although several approaches have been proposed for effectively predicting links, the role of semantics has not been studied in depth. In this work, we tackle the problem of discovering interactions between drugs and targets, and propose SimTransE, a machine learning-based approach that solves this problem effectively. SimTransE relies on translating embeddings to model drug-target interactions and values of similarity across them. Grounded on the vectorial representation of drug-target interactions, SimTransE is able to discover novel drug-target interactions. We empirically study SimTransE using state-of-the-art benchmarks and approaches. Experimental results suggest that SimTransE is competitive with the state of the art, representing, thus, an effective alternative for knowledge discovery in the biomedical domain.
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    A Case for Integrated Data Processing in Large-Scale Cyber-Physical Systems
    (Maui, Hawaii : HICSS, 2019) Glebke, René; Henze, Martin; Wehrle, Klaus; Niemietz, Philipp; Trauth, Daniel; Mattfeld, Patrick; Bergs, Thomas; Bui, Tung X.
    Large-scale cyber-physical systems such as manufacturing lines generate vast amounts of data to guarantee precise control of their machinery. Visions such as the Industrial Internet of Things aim at making this data available also to computation systems outside the lines to increase productivity and product quality. However, rising amounts and complexities of data and control decisions push existing infrastructure for data transmission, storage, and processing to its limits. In this paper, we exemplarily study a fine blanking line which can produce up to 6.2 Gbit/s worth of data to showcase the extreme requirements found in modern manufacturing. We consequently propose integrated data processing which keeps inherently local and small-scale tasks close to the processes while at the same time centralizing tasks relying on more complex decision procedures and remote data sources. Our approach thus allows for both maintaining control of field-level processes and leveraging the benefits of “big data” applications.