Filters

2018 - 20194

More filters

2020 - 20224
Reset filters

Settings

End date: 2019 × Start date: 2010 × DDC: 004 × Publisher: New York City : Association for Computing Machinery ×

Search Results

Now showing 1 - 4 of 4
  • Thumbnail Image
    Item
    Soft Inkjet Circuits: Rapid Multi-Material Fabrication of Soft Circuits using a Commodity Inkjet Printer
    (New York City : Association for Computing Machinery, 2019) Khan, Arshad; Roo, Joan Sol; Kraus, Tobias; Steimle, Jürgen
    Despite the increasing popularity of soft interactive devices, their fabrication remains complex and time consuming. We contribute a process for rapid do-it-yourself fabrication of soft circuits using a conventional desktop inkjet printer. It supports inkjet printing of circuits that are stretchable, ultrathin, high resolution, and integrated with a wide variety of materials used for prototyping. We introduce multi-ink functional printing on a desktop printer for realizing multi-material devices, including conductive and isolating inks. We further present DIY techniques to enhance compatibility between inks and substrates and the circuits' elasticity. This enables circuits on a wide set of materials including temporary tattoo paper, textiles, and thermoplastic. Four application cases demonstrate versatile uses for realizing stretchable devices, e-textiles, body-based and re-shapeable interfaces.
  • Thumbnail Image
    Item
    Hi Doppelgänger: Towards Detecting Manipulation in News Comments
    (New York City : Association for Computing Machinery, 2019) Pennekamp, Jan; Henze, Martin; Hohlfeld, Oliver; Panchenko, Andriy
    Public opinion manipulation is a serious threat to society, potentially influencing elections and the political situation even in established democracies. The prevalence of online media and the opportunity for users to express opinions in comments magnifies the problem. Governments, organizations, and companies can exploit this situation for biasing opinions. Typically, they deploy a large number of pseudonyms to create an impression of a crowd that supports specific opinions. Side channel information (such as IP addresses or identities of browsers) often allows a reliable detection of pseudonyms managed by a single person. However, while spoofing and anonymizing data that links these accounts is simple, a linking without is very challenging. In this paper, we evaluate whether stylometric features allow a detection of such doppelgängers within comment sections on news articles. To this end, we adapt a state-of-the-art doppelgänger detector to work on small texts (such as comments) and apply it on three popular news sites in two languages. Our results reveal that detecting potential doppelgängers based on linguistics is a promising approach even when no reliable side channel information is available. Preliminary results following an application in the wild shows indications for doppelgängers in real world data sets.
  • Thumbnail Image
    Item
    When humans and machines collaborate: Cross-lingual Label Editing in Wikidata
    (New York City : Association for Computing Machinery, 2019) Kaffee, L.-A.; Endris, K.M.; Simperl, E.
    The quality and maintainability of a knowledge graph are determined by the process in which it is created. There are different approaches to such processes; extraction or conversion of available data in the web (automated extraction of knowledge such as DBpedia from Wikipedia), community-created knowledge graphs, often by a group of experts, and hybrid approaches where humans maintain the knowledge graph alongside bots. We focus in this work on the hybrid approach of human edited knowledge graphs supported by automated tools. In particular, we analyse the editing of natural language data, i.e. labels. Labels are the entry point for humans to understand the information, and therefore need to be carefully maintained. We take a step toward the understanding of collaborative editing of humans and automated tools across languages in a knowledge graph. We use Wikidata as it has a large and active community of humans and bots working together covering over 300 languages. In this work, we analyse the different editor groups and how they interact with the different language data to understand the provenance of the current label data.
  • Thumbnail Image
    Item
    Why reinvent the wheel: Let's build question answering systems together
    (New York City : Association for Computing Machinery, 2018) Singh, K.; Radhakrishna, A.S.; Both, A.; Shekarpour, S.; Lytra, I.; Usbeck, R.; Vyas, A.; Khikmatullaev, A.; Punjani, D.; Lange, C.; Vidal, Maria-Esther; Lehmann, J.; Auer, Sören
    Modern question answering (QA) systems need to flexibly integrate a number of components specialised to fulfil specific tasks in a QA pipeline. Key QA tasks include Named Entity Recognition and Disambiguation, Relation Extraction, and Query Building. Since a number of different software components exist that implement different strategies for each of these tasks, it is a major challenge to select and combine the most suitable components into a QA system, given the characteristics of a question. We study this optimisation problem and train classifiers, which take features of a question as input and have the goal of optimising the selection of QA components based on those features. We then devise a greedy algorithm to identify the pipelines that include the suitable components and can effectively answer the given question. We implement this model within Frankenstein, a QA framework able to select QA components and compose QA pipelines. We evaluate the effectiveness of the pipelines generated by Frankenstein using the QALD and LC-QuAD benchmarks. These results not only suggest that Frankenstein precisely solves the QA optimisation problem but also enables the automatic composition of optimised QA pipelines, which outperform the static Baseline QA pipeline. Thanks to this flexible and fully automated pipeline generation process, new QA components can be easily included in Frankenstein, thus improving the performance of the generated pipelines.