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Institution

University of Stirling

EducationStirling, Stirling, United Kingdom
About: University of Stirling is a education organization based out in Stirling, Stirling, United Kingdom. It is known for research contribution in the topics: Population & Context (language use). The organization has 7722 authors who have published 20549 publications receiving 732940 citations. The organization is also known as: Stirling University.


Papers
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Journal ArticleDOI
TL;DR: In this article, the authors estimate the magnitude of these external costs and benefits for the case of renewable technologies in Scotland, a country which has set particularly ambitious targets for expanding renewable energy.

481 citations

Proceedings ArticleDOI
01 Dec 2016
TL;DR: A novel method to extract features from visual and textual modalities using deep convolutional neural networks and significantly outperform the state of the art of multimodal emotion recognition and sentiment analysis on different datasets is presented.
Abstract: Technology has enabled anyone with an Internet connection to easily create and share their ideas, opinions and content with millions of other people around the world. Much of the content being posted and consumed online is multimodal. With billions of phones, tablets and PCs shipping today with built-in cameras and a host of new video-equipped wearables like Google Glass on the horizon, the amount of video on the Internet will only continue to increase. It has become increasingly difficult for researchers to keep up with this deluge of multimodal content, let alone organize or make sense of it. Mining useful knowledge from video is a critical need that will grow exponentially, in pace with the global growth of content. This is particularly important in sentiment analysis, as both service and product reviews are gradually shifting from unimodal to multimodal. We present a novel method to extract features from visual and textual modalities using deep convolutional neural networks. By feeding such features to a multiple kernel learning classifier, we significantly outperform the state of the art of multimodal emotion recognition and sentiment analysis on different datasets.

474 citations

Journal ArticleDOI
TL;DR: To find out what those questions are in the UK, representatives from 28 organizations involved in policy, together with scientists from 10 academic institutions, were asked to generate a list of questions, with the most striking outcome the preference for general questions rather than narrow ones.
Abstract: 1. Evidence-based policy requires researchers to provide the answers to ecological questions that are of interest to policy makers. To find out what those questions are in the UK, representatives from 28 organizations involved in policy, together with scientists from 10 academic institutions, were asked to generate a list of questions from their organizations. 2. During a 2-day workshop the initial list of 1003 questions generated from consulting at least 654 policy makers and academics was used as a basis for generating a short list of 100 questions of significant policy relevance. Short-listing was decided on the basis of the preferences of the representatives from the policy-led organizations. 3. The areas covered included most major issues of environmental concern in the UK, including agriculture, marine fisheries, climate change, ecosystem function and land management. 4. The most striking outcome was the preference for general questions rather than narrow ones. The reason is that policy is driven by broad issues rather than specific ones. In contrast, scientists are frequently best equipped to answer specific questions. This means that it may be necessary to extract the underpinning specific question before researchers can proceed. 5. Synthesis and applications. Greater communication between policy makers and scientists is required in order to ensure that applied ecologists are dealing with issues in a way that can feed into policy. It is particularly important that applied ecologists emphasize the generic value of their work wherever possible.

469 citations


Authors

Showing all 7824 results

NameH-indexPapersCitations
Paul M. Thompson1832271146736
Alan D. Baddeley13746789497
Wolf Singer12458072591
John J. McGrath120791124804
Richard J. Simpson11385059378
David I. Perrett11035045878
Simon P. Driver10945546299
David J. Williams107206062440
Linqing Wen10741270794
John A. Raven10655544382
David Coward10340067118
Stuart J. H. Biddle10248441251
Malcolm T. McCulloch10037136914
Andrew P. Dobson9832244211
Lister Staveley-Smith9559936924
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202357
2022175
20211,041
20201,054
2019916
2018903