Abstract

Stimulating consumers to save water is a challenge and an opportunity for water demand management. Existing ICT systems for behavioural change often do not consider the underlying behavioural determinants in a systematic way. This paper discusses the design of the behavioural change and incentive model combining smart meter data with consumption visualisation and gamified incentive mechanisms to stimulate water saving. We show how the design of such a system can be related to a holistic behavioural change model and how this systematic mapping can inform the design of an integrated incentive model combining different incentive types (virtual, physical, social). The model is implemented in the SmartH2O system and deployed in two pilots. We present the preliminary results for the Swiss pilot, which indicate reduced water consumption, positive user feedback and overall suitability of the designed incentive model.

Abstract-This paper describes the design and development of histoGraph, an interactive tool for explorative visualization and collaborative investigation of historical social networks from multimedia collections. Developed in an interdisciplinary collaboration of computer scientists, historians, HCI researchers and interface designers, the tool aims at supporting historians in the discovery and historical analysis of relationships between people, places and events. A special focus is on the identification and interactive visualization of social relations from historical photo collections through a combination of automatic analysis and expert-based crowdsourcing. The tool design bridges the gap between established network analysis and visualization techniques and traditional hermeneutic research methods in historical research. It integrates visual exploration with hybrid social graph construction, hypothesis formulation and the consultation of digitized primary sources. A formative evaluation of the current prototype, developed as a domainspecific application for historians in the field of European integration points to opportunities and critical factors in applying this approach to support and further current research practices in digital humanities.

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We present a system for the classification of mountain panoramas from user-generated photographs followed by identification and extraction of mountain peaks from those panoramas. We have developed an automatic technique that, given as input a geo-tagged photograph, estimates its FOV (Field Of View) and the direction of the camera using a matching algorithm on the photograph edge maps and a rendered view of the mountain silhouettes that should be seen from the observer's point of view. The extraction algorithm then identifies the mountain peaks present in the photograph and their profiles. We discuss possible applications in social fields like photographs peaks tagging on social portals, augmented reality on mobile devices when viewing a mountain panorama, and generation of collective intelligence systems (such as environmental models) from massive social media collections (e.g. snow water availability maps based on mountain peaks states extracted from photographs hosting services).

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Driven by the needs of customers and industry, online fashion search and analytics are recently gaining much attention. As fashion is mostly expressed by visual content, the analysis of fashion images in online social networks is a rich source of possible insights on evolving trends and customer preferences. Although a plethora of visual content is available, the modeling of clothes’ physics and movement, the implicit semantics in fashion designs, and the subjectivity of their interpretation pose difficulties to fully automated solutions for fashion search and analysis. In this article, we present the design and evaluation of a crowd-powered system for fashion similarity search from Twitter, supporting trend analysis for fashion professionals. The system enables fashion similarity search based on specific human-based similarity criteria. This is achieved by implementing a novel machine--crowd workflow that supports complex tasks requiring highly subjective judgments where multiple true solutions may coexist. We discuss how this leads to a novel class of crowd-powered systems for which the output of the crowd is not used to verify the automatic analysis but is the desired outcome. Finally, we show how this kind of crowd involvement enables a novel kind of similarity search and represents a crucial factor for the acceptance of system results by the end user.