AOL

Abstract: This paper provides a synopsis of a server-driven link prefetching mechanism that we have designed and implemented for the Mozilla web browser, a popular Open Source web browser. The mechanism depends on the origin server or an intermediate proxy server determining the best set of documents for the browser to prefetch. The browser follows prefetch directives provided by the server, either embedded in an HTML document using the 〈LINK〉 tag or specified via Link HTTP response headers. The browser determines when best to prefetch the specified URLs based on its own heuristics. In this paper, we describe the mechanism and discuss some of the practical issues that impacted its design and implementation.

Abstract: This article introduces a simple mapping tool called Grassroots, a software product from a longitudinal study examining the use of information communication technologies and knowledge work in communities. Grassroots is an asset-based mapping tool made possible by the Web 2.0 movement, a movement which allows for the creation of more adaptable interfaces by making data and underlying database structures more openly available via syndication and open source software. This article forwards three arguments. First is an argument about the nature of the knowledge work of everyday life, or an argument about the complex technological and rhetorical tasks necessary to solve commonplace problems through writing. Second is an argument about specific technologies and genres of community-based knowledge work, about why making maps is such an essential genre, and about why making asset maps is potentially transformative. Third is an argument about the making of Grassroots itself; a statement about how we should best ex...

Abstract: A comprehensive understanding of river dynamics requires the grain size distribution of bed sediments and its variation across different temporal and spatial scales. Several techniques are already available for grain size assessment based on field and remotely sensed data. However, the existing methods are only applicable on small spatial scales and on short time scales. Thus, the operational measurement of grain size distribution of riverbed sediments at the catchment scale remains an open problem. A solution could be the use of satellite images as the main imaging platform. However, this would entail retrieving information at sub-pixel scales. In this study, we propose a new approach to retrieve sub-pixel scale grain size class information from Copernicus Sentinel-2 imagery building upon a new image-based grain size mapping procedure. Three Italian gravel-bed rivers featuring different morphologies were selected for unmanned aerial vehicle (UAV) acquisitions, field surveys and laboratory analysis meant to serve as ground truth grain size data, ranging from medium sand to coarse gravel. Grain size maps on the river bars were generated in each study site by exploiting image texture measurements, upscaled and co-registered with Sentinel-2 data resolution. Relationships between the grain sizes measured and the reflectance values in Sentinel-2 imagery were analysed using a machine learning framework. Results show statistically significant predictive models (MAE of ±8.34 mm and R2 = 0.92). The trained model was applied on 300 km of the Po River in Italy and allowed us to identify the gravel–sand transition occurring along this river length. Therefore, the approach presented here—based on freely available satellite data calibrated by low-cost drone-derived imagery—represents a promising step towards an automated surface mean grain size mapping over long river length, easily repeated through time for monitoring purposes.

Abstract: To prevent spam, spyware, and other intrusions, network operators and software providers deploy trusted intermediary technologies to protect their end users and networks. These technologies can be designed to anticipate legal challenges that may be raised by the companies and other actors whose behaviors they inhibit