Web content is notoriously difficult to capture on a printed page due to inconsistent and undesired results. Items that users may not want to print, such as media, navigation menus and more show up on their page. Other items that they may care about are truncated or spread across several pages. Some tools exist to help users with what is printed, but they often are cumbersome to use or are costly for a company to maintain. Therefore, we introduce PrintMonkey, which allows users to write their own printing templates and share them with others on the web. No modifications to the original webpages are required and users with less development experience can use and develop templates. A comparison with four alternative solutions reveals the concrete ways in which PrintMonkey improves upon existing approaches in terms of functionality, customizability and scalability.

Multi-core programming presents developers with a dramatic paradigm shift. Whereas sequential programming largely allowed the decoupling of source from underlying architecture, it is now impossible to develop new patterns and abstractions in isolation from issues of modern hardware utilization. Synchronization and coordination are now manifested at all levels of the software stack, and developers currently lack the essential tools to even partially automate reasoning techniques and system configuration management. As a first stage to addressing this problem, this paper proposes a framework for a tool suite designed to partially automate the acquisition and management of static system visualization in a feedback loop with dynamic execution properties. This model enables developers to find a best fit system configuration, potentially reconciling resource contention and utilization tensions that are critical to multi-core platforms. The application of a prototype of this suite, Deja View, demonstrates how tool support can aid reasoning about causally related sets of changes across system artifacts.

This article reports on a temporal tagger for Korean based on a Korean extension of the TIDES TIMEX2 guidelines. The extension, which primarily addresses the idiosyncrasies of Korean morphology, shows high inter-annotator reliability (0.893 F-measure for tag extent) when applied to a corpus of Korean newspaper articles. A machine-learning approach based on rote learning from a human-edited, automatically-derived dictionary of temporal expressions is compared with a second approach that adds manual patterns, and a third onethat tries to learn the patterns. Results for the first two are promising (0.87 F-measure for tag extent). Overall, the article shows that rote learning approaches can be very useful when language-specific features such as morphology are taken into account.