347,204 research outputs found

    Detecting and locating trending places using multimodal social network data

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    This paper presents a machine learning-based classifier for detecting points of interest through the combined use of images and text from social networks. This model exploits the transfer learning capabilities of the neural network architecture CLIP (Contrastive Language-Image Pre-Training) in multimodal environments using image and text. Different methodologies based on multimodal information are explored for the geolocation of the places detected. To this end, pre-trained neural network models are used for the classification of images and their associated texts. The result is a system that allows creating new synergies between images and texts in order to detect and geolocate trending places that has not been previously tagged by any other means, providing potentially relevant information for tasks such as cataloging specific types of places in a city for the tourism industry. The experiments carried out reveal that, in general, textual information is more accurate and relevant than visual cues in this multimodal setting.Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. This research has been partially funded by project “Desarrollo de un ecosistema de datos abiertos para transformar el sector turístico” (GVA-COVID19/2021/103) funded by Conselleria de Innovación, Universidades, Ciencia y Sociedad Digital de la Generalitat Valenciana, “A way of making Europe” European Regional Development Fund (ERDF) and MCIN/AEI/10.13039/501100011033 for supporting this work under the “CHAN-TWIN” project (grant TED2021-130890B-C21) and the HORIZON-MSCA-2021-SE-0 action number: 101086387, REMARKABLE, Rural Environmental Monitoring via ultra wide-ARea networKs And distriButed federated Learning. We also would like to thank Nvidia for their generous hardware donations that made these experiments possible

    Seeing the smart city on Twitter: Colour and the affective territories of becoming smart

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    This paper pays attention to the immense and febrile field of digital image files which picture the smart city as they circulate on the social media platform Twitter. The paper considers tweeted images as an affective field in which flow and colour are especially generative. This luminescent field is territorialised into different, emergent forms of becoming ‘smart’. The paper identifies these territorialisations in two ways: firstly, by using the data visualisation software ImagePlot to create a visualisation of 9030 tweeted images related to smart cities; and secondly, by responding to the affective pushes of the image files thus visualised. It identifies two colours and three ways of affectively becoming smart: participating in smart, learning about smart, and anticipating smart, which are enacted with different distributions of mostly orange and blue images. The paper thus argues that debates about the power relations embedded in the smart city should consider the particular affective enactment of being smart that happens via social media. More generally, the paper concludes that geographers must pay more attention to the diverse and productive vitalities of social media platforms in urban life and that this will require experiment with methods that are responsive to specific digital qualities

    The memory space: Exploring future uses of Web 2.0 and mobile internet through design interventions.

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    The QuVis Quantum Mechanics Visualization project aims to address challenges of quantum mechanics instruction through the development of interactive simulations for the learning and teaching of quantum mechanics. In this article, we describe evaluation of simulations focusing on two-level systems developed as part of the Institute of Physics Quantum Physics resources. Simulations are research-based and have been iteratively refined using student feedback in individual observation sessions and in-class trials. We give evidence that these simulations are helping students learn quantum mechanics concepts at both the introductory and advanced undergraduate level, and that students perceive simulations to be beneficial to their learning.Comment: 15 pages, 5 figures, 1 table; accepted for publication in the American Journal of Physic

    Beyond 'Global Production Networks': Australian Fashion Week's Trans-Sectoral Synergies

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    When studies of industrial organisation are informed by commodity chain, actor network, or global production network theories and focus on tracing commodity flows, social networks, or a combination of the two, they can easily overlook the less routine trans-sectoral associations that are crucial to the creation and realisation of value. This paper shifts attention to identifying the sites at which diverse specialisations meet to concentrate and amplify mutually reinforcing circuits of value. These valorisation processes are demonstrated in the case of Australian Fashion Week, an event in which multiple interests converge to synchronize different expressions of fashion ideas, actively construct fashion markets and enhance the value of a diverse range of fashionable commodities. Conceptualising these interconnected industries as components of a trans-sectoral fashion complex has implications for understanding regional development, world cities, production location, and the manner in which production systems “touch down” in different places
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