79 research outputs found

    QR code awareness in Stockholm, Sweden

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    This tech report describes the findings of a street survey on awareness of QR codes (2D barcodes) of the general public in Stockholm, Sweden. 108 passers-by were surveyed. Of these participants, a large majority (77%) did not recognize a QR code, and 8% reported seeing such a code before, but did not know it could be scanned using a mobile phone app. Only 15% knew what the shown QR code was, and that it could be read using a QR code reader on a mobile phone. The awareness of QR codes by the general public could be considered rather low, and their utility in Swedish public settings is currently debatable

    Services as Materials: Using Mashups for Research

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    Using existing services as development and research materials can greatly reduce development burdens. However, using mashups and existing services has consequences that go beyond the technical realm. We present our ongoing experience with developing and promoting a mobile mash-up implemented in the mobile web browser: Spotisquare. Spotisquare is a mash-up of the location-based service foursquare and music streaming service Spotify. We discuss advantages and tradeoffs of using existing services and the mobile mash-up process, including interaction model choices, as well as validity and representational issues

    Describing and Understanding Neighborhood Characteristics through Online Social Media

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    Geotagged data can be used to describe regions in the world and discover local themes. However, not all data produced within a region is necessarily specifically descriptive of that area. To surface the content that is characteristic for a region, we present the geographical hierarchy model (GHM), a probabilistic model based on the assumption that data observed in a region is a random mixture of content that pertains to different levels of a hierarchy. We apply the GHM to a dataset of 8 million Flickr photos in order to discriminate between content (i.e., tags) that specifically characterizes a region (e.g., neighborhood) and content that characterizes surrounding areas or more general themes. Knowledge of the discriminative and non-discriminative terms used throughout the hierarchy enables us to quantify the uniqueness of a given region and to compare similar but distant regions. Our evaluation demonstrates that our model improves upon traditional Naive Bayes classification by 47% and hierarchical TF-IDF by 27%. We further highlight the differences and commonalities with human reasoning about what is locally characteristic for a neighborhood, distilled from ten interviews and a survey that covered themes such as time, events, and prior regional knowledgeComment: Accepted in WWW 2015, 2015, Florence, Ital
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