Improving Accessibility Through the Visual Structure of Web Contents

Author(s):  
Masahiro Watanabe ◽  
Daisuke Asai ◽  
Yoko Asano
Semiotica ◽  
2018 ◽  
Vol 2018 (220) ◽  
pp. 123-153
Author(s):  
Andrea Rocci ◽  
Sabrina Mazzali-Lurati ◽  
Chiara Pollaroli

AbstractThe aim of this article is to contribute to the theoretical development of multimodal metonymy and the argumentative and rhetorical role that the trope can fulfil in multimodal advertising campaigns. A model for the analysis of multimodal tropes in page-based advertising messages is developed by drawing insights from different disciplines. This model involves the identification of the elementary and layout components of the message, the description of its multimodal structure (in terms of the visual structure and the contribution of the verbal component), the reconstruction of its meaning operation, and the reconstruction of its enthymematic structure. In particular, the meaning operation is reconstructed by the employment of Conceptual Integration Theory, which we have slightly revised in order to better account for metonymical mappings. The enthymematic structure is reconstructed following the Argumentum Model of Topics, a model of argument schemes that enables one to make explicit the contextual and the logical dimensions of arguments. Based on the tenets of the two frameworks, we claim that multimodal metonymy condenses and gives access to a complex chain of connections, which mirrors the argumentation the audience is invited to infer. This argumentation is based on causal schemes of reasoning. This claim results in the in-depth analysis of both a billboard belonging to an anti-AIDS campaign and a social campaign by Greenpeace against the use of environmental-damaging paper for toy packages by Mattel.


2013 ◽  
Vol 28 (4) ◽  
pp. 640-659 ◽  
Author(s):  
M. Grassi ◽  
C. Morbidoni ◽  
M. Nucci ◽  
S. Fonda ◽  
F. Piazza
Keyword(s):  

2004 ◽  
Vol 45 (1) ◽  
pp. 19-34 ◽  
Author(s):  
Norihito Fujita ◽  
Yuichi Ishikawa ◽  
Atsushi Iwata ◽  
Rauf Izmailov

2013 ◽  
Vol 427-429 ◽  
pp. 2554-2557
Author(s):  
Jin Kun Pan ◽  
Dong Sheng Li

With the popularity of location-based services, Web contents are being geo-tagged and spatial keyword queries that retrieve objects satisfying both spatial and keyword conditions are gaining in prevalence. The existing spatial keyword queries focus on exact match or prefix match of the keywords cannot satisfy the demand of wildcard based imprecise match in many realistic scenes. Aiming to solve this problem, two methods which are fit for different situation are put forward: the inverted file and R-tree integrated index which fits for the situation that requires high time efficiency and the Prefix Bloom Filter and R-tree integrated index which fits for the situation requiring high space efficiency. The effectiveness of the two indexes is valid through experiments.


1999 ◽  
Vol 88 (2) ◽  
pp. 515-530 ◽  
Author(s):  
Thomas M. Nelson ◽  
Thomy H. Nilsson ◽  
David J. Piercey ◽  
Thomas Johnson ◽  
Jorge Frascara ◽  
...  
Keyword(s):  

Author(s):  
Abdul Razaque ◽  
Bakhytzhan Valiyev ◽  
Bandar Alotaibi ◽  
Munif Alotaibi ◽  
Saule Amanzholova ◽  
...  

The Dark Web is known as a place triggering a variety of criminal activities. Anonymization techniques enable illegal operations, leading to the loss of confidential information and its further use as bait, a trade product or even a crime tool. Despite technical progress, there is still not enough awareness of the Dark Web and its secret activity. In this study, we introduced the Dark Web Enhanced Analysis (DWEA), in order to analyze and gather information about the content accessed on the Dark Net based on data characteristics. The research was performed to identify how the Dark Web has been influenced by recent global events, such as the COVID-19 epidemic. The research included the usage of a crawler, which scans the network and collects data for further analysis with machine learning. The result of this work determines the influence of the COVID-19 epidemic on the Dark Net.


Author(s):  
David Martín ◽  
Ortzi Torices ◽  
Hugo Salas ◽  
Carlos Lamsfus ◽  
Aurkene Alzua-Sorzabal

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