563 research outputs found
Collaborative Landmark Mining with a Gamification Approach
In the field of pedestrian navigation some systems use prominent identifying features, so called landmarks. Attributes of high-quality landmarks are recognizability, actuality, uniqueness and noticeability. One of the challenges of this kind of navigation systems is to collect and evaluate landmarks with consistent quality. The system we developed solves these struggles with a crowdsourcing approach. We combine this with gamification elements in order to reach many users and to assure long-term motivation. Our system shows images of existing landmarks to the player, which he is afterwards asked to assign to a map of the university. Depending on the distance of his guess to the real position the player earns points. The application strives to encourage users to upload and rate pictures of existing landmarks. A multiplayer mode which allows challenging other users keeps them involved. In contrast to other products, our system does not rely on localization via GPS. Another goal was to implement a self-running system with a minimal amount of dedicated administration needed. Therefore the users with the highest scores are rating the submitted content
Incentive Mechanisms for Participatory Sensing: Survey and Research Challenges
Participatory sensing is a powerful paradigm which takes advantage of
smartphones to collect and analyze data beyond the scale of what was previously
possible. Given that participatory sensing systems rely completely on the
users' willingness to submit up-to-date and accurate information, it is
paramount to effectively incentivize users' active and reliable participation.
In this paper, we survey existing literature on incentive mechanisms for
participatory sensing systems. In particular, we present a taxonomy of existing
incentive mechanisms for participatory sensing systems, which are subsequently
discussed in depth by comparing and contrasting different approaches. Finally,
we discuss an agenda of open research challenges in incentivizing users in
participatory sensing.Comment: Updated version, 4/25/201
Motivational Principles and Personalisation Needs for Geo-Crowdsourced Intangible Cultural Heritage Mobile Applications
Whether it’s for altruistic reasons, personal gains, or third party’s interests, users are influenced by different kinds of motivations when making use of mobile geo-crowdsourcing applications (geoCAs). These reasons, extrinsic and/or intrinsic, must be factored in when evaluating the use intention of these applications and how effective they are. A functional geoCA, particularly if designed for Volunteered Geographic Information (VGI), is the one that persuades and engages its users, by accounting for their diversity of needs across a period of time. This paper explores a number of proven and novel motivational factors destined for the preservation and collection of Intangible Cultural Heritage (ICH) through geoCAs. By providing an overview of personalisation research and digital behaviour interventions for geo-crowdsoured ICH, the paper examines the most relevant usability and trigger factors for different crowd users, supported by a range of technology-based principles. In addition, we present the case of StoryBee, a mobile geoCA designed for “crafting stories” by collecting and sharing users’ generated content based on their location and favourite places. We conclude with an open-ended discussion about the ongoing challenges and opportunities arising from the deployment of geoCAs for ICH
Team Video Gaming for Team Building: Effects on Team Performance
Teams rapidly form and dissolve in organizations to solve specific problems that require diverse skills and experience. For example, in the information systems context, cross-functional and project-based teams that comprise a mix of personnel who temporarily work away from their usual functional groups (best perform agile software development (Barlow et al., 2011; Keith, Demirkan, & Goul, 2013). These newly formed work teams need to become productive as quickly as possible. Team video gaming (TVG) has emerged as a potential team-building activity. When new teammates play a collaborative video game, they engage in cooperative and challenging goals while they enjoy the games. Although research has shown that video games can promote learning and recreation, it has not investigated the effects of commercial video games on subsequent work-team performance. Better understanding this issue will provide insights into how to rapidly develop cohesion among newly formed work teams and, thus, lead to greater team performance. We examined this issue through a laboratory experiment. We found that teams in the TVG treatment demonstrated a 20 percent productivity improvement in subsequent tasks (in our case, a team-based geocaching scavenger hunt) over teams that participated in traditional team-building activities
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Interpretable classification of Alzheimer's disease pathologies with a convolutional neural network pipeline.
Neuropathologists assess vast brain areas to identify diverse and subtly-differentiated morphologies. Standard semi-quantitative scoring approaches, however, are coarse-grained and lack precise neuroanatomic localization. We report a proof-of-concept deep learning pipeline that identifies specific neuropathologies-amyloid plaques and cerebral amyloid angiopathy-in immunohistochemically-stained archival slides. Using automated segmentation of stained objects and a cloud-based interface, we annotate > 70,000 plaque candidates from 43 whole slide images (WSIs) to train and evaluate convolutional neural networks. Networks achieve strong plaque classification on a 10-WSI hold-out set (0.993 and 0.743 areas under the receiver operating characteristic and precision recall curve, respectively). Prediction confidence maps visualize morphology distributions at high resolution. Resulting network-derived amyloid beta (Aβ)-burden scores correlate well with established semi-quantitative scores on a 30-WSI blinded hold-out. Finally, saliency mapping demonstrates that networks learn patterns agreeing with accepted pathologic features. This scalable means to augment a neuropathologist's ability suggests a route to neuropathologic deep phenotyping
Advanced predictive-analysis-based decision support for collaborative logistics networks
Purpose – The purpose of this paper is to examine challenges and potential of big data in heterogeneous business networks and relate these to an implemented logistics solution. Design/methodology/approach – The paper establishes an overview of challenges and opportunities of current significance in the area of big data, specifically in the context of transparency and processes in heterogeneous enterprise networks. Within this context, the paper presents how existing components and purpose-driven research were combined for a solution implemented in a nationwide network for less-than-truckload consignments. Findings – Aside from providing an extended overview of today’s big data situation, the findings have shown that technical means and methods available today can comprise a feasible process transparency solution in a large heterogeneous network where legacy practices, reporting lags and incomplete data exist, yet processes are sensitive to inadequate policy changes. Practical implications – The means introduced in the paper were found to be of utility value in improving process efficiency, transparency and planning in logistics networks. The particular system design choices in the presented solution allow an incremental introduction or evolution of resource handling practices, incorporating existing fragmentary, unstructured or tacit knowledge of experienced personnel into the theoretically founded overall concept. Originality/value – The paper extends previous high-level view on the potential of big data, and presents new applied research and development results in a logistics application
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Advances in Technology Enhanced Learning
‘Advances in Technology Enhanced Learning’ presents a range of research projects which aim to explore how to make engagement in learning (and teaching) more passionate. This interactive and experimental resource discusses innovations which pave the way to open collaboration at scale. The book introduces methodological and technological breakthroughs via twelve chapters to learners, instructors, and decision-makers in schools, universities, and workplaces.
The Open University's Knowledge Media Institute and the EU TELMap project have brought together the luminaries from the European research area to showcase their vision of the future of learning with technology via their recent research project work. The projects discussed range widely over the Technology Enhanced Learning area from: environments for responsive open learning, work-based reflection, work-based social creativity, serious games and many more
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