123,488 research outputs found
Explaining Opposition to Refugee Resettlement: The Role of NIMBYism and Perceived Threats
One week after President Donald Trump signed a controversial executive order to reduce the influx of refugees to the United States, we conducted a survey experiment to understand American citizens ’ attitudes toward refugee resettlement. Specifically, we evaluated whether citizens consider the geographic context of the resettlement program (that is, local versus national) and the degree to which they are swayed by media frames that increasingly associate refugees with terrorist thre ats. Our findings highlight a collective action problem: Participants are consistently less supportive of resettlement within their own communities than resettlement elsewhere in the country. This pattern holds across all measured demographic, political, and geographic subsamples within our data. Furthermore, our results demonstrate that threatening media frames significantly reduce support for both national and local resettlement. Conversely, media frames rebutting the threat posed by refugees have no sig- nificant effect. Finally, the results indicate that par ticipants in refugee-dense counties are less responsive to threatening frames, suggesting that proximity to previously settled refugees may reduce the impact of perceived security threats
Large-Scale Mapping of Human Activity using Geo-Tagged Videos
This paper is the first work to perform spatio-temporal mapping of human
activity using the visual content of geo-tagged videos. We utilize a recent
deep-learning based video analysis framework, termed hidden two-stream
networks, to recognize a range of activities in YouTube videos. This framework
is efficient and can run in real time or faster which is important for
recognizing events as they occur in streaming video or for reducing latency in
analyzing already captured video. This is, in turn, important for using video
in smart-city applications. We perform a series of experiments to show our
approach is able to accurately map activities both spatially and temporally. We
also demonstrate the advantages of using the visual content over the
tags/titles.Comment: Accepted at ACM SIGSPATIAL 201
MusA: Using Indoor Positioning and Navigation to Enhance Cultural Experiences in a museum
In recent years there has been a growing interest into the use of multimedia mobile guides in museum environments. Mobile devices have the capabilities to detect the user context and to provide pieces of information suitable to help visitors discovering and following the logical and emotional connections that develop during the visit. In this scenario, location based services (LBS) currently represent an asset, and the choice of the technology to determine users' position, combined with the definition of methods that can effectively convey information, become key issues in the design process. In this work, we present MusA (Museum Assistant), a general framework for the development of multimedia interactive guides for mobile devices. Its main feature is a vision-based indoor positioning system that allows the provision of several LBS, from way-finding to the contextualized communication of cultural contents, aimed at providing a meaningful exploration of exhibits according to visitors' personal interest and curiosity. Starting from the thorough description of the system architecture, the article presents the implementation of two mobile guides, developed to respectively address adults and children, and discusses the evaluation of the user experience and the visitors' appreciation of these application
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Evaluating the effectiveness of representing numeric information through abstract graphics in 3D desktop virtual environments
Integrating Multiple 3D Views through Frame-of-reference Interaction
Frame-of-reference interaction consists of a unified set of 3D interaction techniques for exploratory navigation of large virtual spaces in nonimmersive environments. It is based on a conceptual framework that considers navigation from a cognitive perspective, as a way of facilitating changes in user attention from one reference frame to another, rather than from the mechanical perspective of moving a camera between different points of interest. All of our techniques link multiple frames of reference in some meaningful way. Some techniques link multiple windows within a zooming environment while others allow seamless changes of user focus between static objects, moving objects, and groups of moving objects. We present our techniques as they are implemented in GeoZui3D, a geographic visualization system for ocean data
Searching Data: A Review of Observational Data Retrieval Practices in Selected Disciplines
A cross-disciplinary examination of the user behaviours involved in seeking
and evaluating data is surprisingly absent from the research data discussion.
This review explores the data retrieval literature to identify commonalities in
how users search for and evaluate observational research data. Two analytical
frameworks rooted in information retrieval and science technology studies are
used to identify key similarities in practices as a first step toward
developing a model describing data retrieval
Fog_Hive© : 3D fog collection along the coastal Atacama desert
The provision of drinking water turns out to be one of the great challenges for the future because central water supply systems cannot technically or logistically be implemented. FogHive©'s main aim is stopping desertification by repairing endangered fog oases ecosystems, and harvesting water for drinking and irrigation and fostering potential inhabitation in many arid coasts such as Chile, Peru and others latitudes. FogHive© is resilient to different climatic contexts and can dynamically response to the different and intermittent prevailing wind directions by keeping the screen ratio of 1:1 or 1:2. It is an adaptable and lightweight design with emphasis in optimal frame types, forms, structural and surface sizes, structural and constructional specifications made with aluminium, galvanised steel or timber. FogHive© employs hydrophobic meshes and a deployable space-frame to intersect atmospheric water and then harvest it for drinking and irrigation. FogHive© has been tested throughout climatic simulations in the fog oasis of Alto Patache, Atacama Desert (2010). It also performs like a shading/cooling device and a soil humidifier for greenery or crop. Being a transformable construction, it can easily be transported and installed. Its footprint is hexagonal (6m side) which is resistant against strong winds and ‘aerodynamic’ to the landscape. FogHive© consists of a water-repellent skin facing prevailing winds and a shading device facing the Equator. The water collector, filtering and irrigation network considers local structural materials and techniques. Regarding conventional two-dimensional fog collection, FogHive© upgrades the following aspects: 1. Increasing rate and yield of advection fog by taking into account harvesting rate and climatic parameters; 2. Structural reinforcement of fog collectors through lightweight and deployable space-frames; 3. Reducing installation and maintenance of fog collection; 4. Lowering physical impacts on surrounding
Rhythmic Representations: Learning Periodic Patterns for Scalable Place Recognition at a Sub-Linear Storage Cost
Robotic and animal mapping systems share many challenges and characteristics:
they must function in a wide variety of environmental conditions, enable the
robot or animal to navigate effectively to find food or shelter, and be
computationally tractable from both a speed and storage perspective. With
regards to map storage, the mammalian brain appears to take a diametrically
opposed approach to all current robotic mapping systems. Where robotic mapping
systems attempt to solve the data association problem to minimise
representational aliasing, neurons in the brain intentionally break data
association by encoding large (potentially unlimited) numbers of places with a
single neuron. In this paper, we propose a novel method based on supervised
learning techniques that seeks out regularly repeating visual patterns in the
environment with mutually complementary co-prime frequencies, and an encoding
scheme that enables storage requirements to grow sub-linearly with the size of
the environment being mapped. To improve robustness in challenging real-world
environments while maintaining storage growth sub-linearity, we incorporate
both multi-exemplar learning and data augmentation techniques. Using large
benchmark robotic mapping datasets, we demonstrate the combined system
achieving high-performance place recognition with sub-linear storage
requirements, and characterize the performance-storage growth trade-off curve.
The work serves as the first robotic mapping system with sub-linear storage
scaling properties, as well as the first large-scale demonstration in
real-world environments of one of the proposed memory benefits of these
neurons.Comment: Pre-print of article that will appear in the IEEE Robotics and
Automation Letter
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