Institution
City University London
Education•London, United Kingdom•
About: City University London is a education organization based out in London, United Kingdom. It is known for research contribution in the topics: Population & Context (language use). The organization has 5735 authors who have published 17285 publications receiving 453290 citations.
Papers published on a yearly basis
Papers
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TL;DR: The findings are interpreted as providing further support for the distinction between episodic memory and other memory systems, and also as providing more qualified support for theories that assume that recognition memory entails two components, one of which may also give rise to priming effects in implicit memory.
Abstract: The functional relationship between memory and consciousness was investigated in two experiments in which subjects indicated when recognizing an item whether they could consciously recollect its prior occurrence in the study list or recognized it on some other basis, in the absence of conscious recollection. Low-frequency words, relative to high-frequency words, enhanced recognition accompanied by conscious recollection but did not influence recognition in the absence of conscious recollection. By contrast, nonwords compared with words enhanced recognition in the absence of conscious recollection and reduced recognition accompanied by conscious recollection. A third experiment showed that confidence judgments in recognizing nonword targets corresponded with recognition performance, not with recollective experience. These measures of conscious awareness therefore tap qualitatively different components of memory, not some unitary dimension such as “trace strength.” The findings are interpreted as providing further support for the distinction between episodic memory and other memory systems, and also as providing more qualified support for theories that assume that recognition memory entails two components, one of which may also give rise to priming effects in implicit memory.
478 citations
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TL;DR: In this paper, the authors identify four foundational elements that characterize this emerging neoconfigurational perspective: 1) conceptualizing cases as set theoretic configurations; 2) calibrating cases' memberships into sets; 3) viewing causality in terms of necessity and sufficiency relations between sets; and 4) conducting counterfactual analysis of unobserved configurations.
477 citations
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TL;DR: This review gives an insight into the strengths and shortcomings of the known research methodologies and provides a platform, to the researchers and practitioners, toward proposing the next-generation Android security, analysis, and malware detection techniques.
Abstract: Smartphones have become pervasive due to the availability of office applications, Internet, games, vehicle guidance using location-based services apart from conventional services such as voice calls, SMSes, and multimedia services. Android devices have gained huge market share due to the open architecture of Android and the popularity of its application programming interface (APIs) in the developer community. Increased popularity of the Android devices and associated monetary benefits attracted the malware developers, resulting in big rise of the Android malware apps between 2010 and 2014. Academic researchers and commercial antimalware companies have realized that the conventional signature-based and static analysis methods are vulnerable. In particular, the prevalent stealth techniques, such as encryption, code transformation, and environment-aware approaches, are capable of generating variants of known malware. This has led to the use of behavior-, anomaly-, and dynamic-analysis-based methods. Since a single approach may be ineffective against the advanced techniques, multiple complementary approaches can be used in tandem for effective malware detection. The existing reviews extensively cover the smartphone OS security. However, we believe that the security of Android, with particular focus on malware growth, study of antianalysis techniques, and existing detection methodologies, needs an extensive coverage. In this survey, we discuss the Android security enforcement mechanisms, threats to the existing security enforcements and related issues, malware growth timeline between 2010 and 2014, and stealth techniques employed by the malware authors, in addition to the existing detection methods. This review gives an insight into the strengths and shortcomings of the known research methodologies and provides a platform, to the researchers and practitioners, toward proposing the next-generation Android security, analysis, and malware detection techniques.
473 citations
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Masaryk University1, Royal Institute of Technology2, University of Freiburg3, Heidelberg University4, Erasmus University Medical Center5, Stanford University6, Leiden University Medical Center7, Delft University of Technology8, Agency for Science, Technology and Research9, University of Nottingham10, The University of Nottingham Ningbo China11, Pasteur Institute12, City University London13, Karlsruhe Institute of Technology14, University of Porto15, University of Zurich16, Max Planck Society17, Charles III University of Madrid18, University of Navarra19
TL;DR: It is found that methods that either take prior information into account using learning strategies or analyze cells in a global spatiotemporal video context performed better than other methods under the segmentation and tracking scenarios included in the Cell Tracking Challenge.
Abstract: We present a combined report on the results of three editions of the Cell Tracking Challenge, an ongoing initiative aimed at promoting the development and objective evaluation of cell segmentation and tracking algorithms. With 21 participating algorithms and a data repository consisting of 13 data sets from various microscopy modalities, the challenge displays today's state-of-the-art methodology in the field. We analyzed the challenge results using performance measures for segmentation and tracking that rank all participating methods. We also analyzed the performance of all of the algorithms in terms of biological measures and practical usability. Although some methods scored high in all technical aspects, none obtained fully correct solutions. We found that methods that either take prior information into account using learning strategies or analyze cells in a global spatiotemporal video context performed better than other methods under the segmentation and tracking scenarios included in the challenge.
468 citations
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University of Essex1, University of Cambridge2, International Center for Tropical Agriculture3, United States Department of Agriculture4, University of Wisconsin-Madison5, CABI6, Department for International Development7, University of East Anglia8, University of Otago9, Agricultural & Applied Economics Association10, Agriculture and Horticulture Development Board11, University of Bern12, Bangor University13, University of Matanzas14, University of Oxford15, Chiang Mai University16, University of Sussex17, Secretariat of the Pacific Community18, Millennium Institute19, CGIAR20, Fara21, Ohio State University22, City University London23, International Union for Conservation of Nature and Natural Resources24, Department for Business, Innovation and Skills25, International Water Management Institute26, Wageningen University and Research Centre27, Indian Institute of Science28, University of the Philippines Los Baños29, University College Cork30, Fujian Normal University31, South China Agricultural University32, World Agroforestry Centre33, University of Aberdeen34, University of Colorado Boulder35, University of Victoria36, University of California, Davis37, International Institute for Environment and Development38, United Nations39, Royal Society for the Protection of Birds40
TL;DR: The UK Government's Foresight Global Food and Farming Futures project as mentioned in this paper aims to improve dialogue and understanding between agricultural research and policy by identifying the 100 most important questions for global agriculture.
Abstract: Despite a significant growth in food production over the past half-century, one of the most important challenges facing society today is how to feed an expected population of some nine billion by the middle of the 20th century. To meet the expected demand for food without significant increases in prices, it has been estimated that we need to produce 70-100 per cent more food, in light of the growing impacts of climate change, concerns over energy security, regional dietary shifts and the Millennium Development target of halving world poverty and hunger by 2015. The goal for the agricultural sector is no longer simply to maximize productivity, but to optimize across a far more complex landscape of production, rural development, environmental, social justice and food consumption outcomes. However, there remain significant challenges to developing national and international policies that support the wide emergence of more sustainable forms of land use and efficient agricultural production. The lack of information flow between scientists, practitioners and policy makers is known to exacerbate the difficulties, despite increased emphasis upon evidence-based policy. In this paper, we seek to improve dialogue and understanding between agricultural research and policy by identifying the 100 most important questions for global agriculture. These have been compiled using a horizon-scanning approach with leading experts and representatives of major agricultural organizations worldwide. The aim is to use sound scientific evidence to inform decision making and guide policy makers in the future direction of agricultural research priorities and policy support. If addressed, we anticipate that these questions will have a significant impact on global agricultural practices worldwide, while improving the synergy between agricultural policy, practice and research. This research forms part of the UK Government's Foresight Global Food and Farming Futures project.
467 citations
Authors
Showing all 5822 results
Name | H-index | Papers | Citations |
---|---|---|---|
Andrew M. Jones | 103 | 764 | 37253 |
F. Rauscher | 100 | 605 | 36066 |
Thorsten Beck | 99 | 373 | 62708 |
Richard J. K. Taylor | 91 | 1543 | 43893 |
Christopher N. Bowman | 90 | 639 | 38457 |
G. David Batty | 88 | 451 | 23826 |
Xin Zhang | 87 | 1714 | 40102 |
Richard J. Cook | 84 | 571 | 28943 |
Hugh Willmott | 82 | 310 | 26758 |
Scott Reeves | 82 | 441 | 27470 |
Sarah-Jayne Blakemore | 81 | 211 | 29660 |
Mats Alvesson | 78 | 267 | 38248 |
W. John Edmunds | 75 | 252 | 24018 |
Sheng Chen | 71 | 688 | 27847 |
Christopher J. Taylor | 71 | 415 | 30948 |