Criminal Conversations: Rogues, Words and the World in the Work of Daniel Defoe

Even as Daniel Defoe's roguish protagonists notably Moll Flanders and Colonel Jack try to separate themselves from illicit itinerants, they are implicated further in deviance. Moll and Jack both embody and exploit ambiguous moral and spatial arrangements, and use hybrid linguistic formulations, all of which collocate the roguish and the reputable. By brilliantly realizing this interpenetration of words and worlds, Defoe problematises eighteenth-century efforts to demarcate the illicit and itinerant along the lines of space, rank, gender and language. Such efforts facilitated deviant mobility as much as they demonised it. Much scholarship has attended to Defoe's representations of criminality and poverty. This article develops such research to re-position him in a tradition of rogue-writing that stylishly problematises normative discriminatory practices.

Extracting Policy Positions from Political Texts Using words as Data

We present a new way of extracting policy positions from political texts that treats texts not as discourses to be understood and interpreted but rather, as data in the form of words. We compare this approach to previous methods of text analysis and use it to replicate published estimates of the policy positions of political parties in Britain and Ireland, on both economic and social policy dimensions. We “export” the method to a non-English-language environment, analyzing the policy positions of German parties, including the PDS as it entered the former West German party system. Finally, we extend its application beyond the analysis of party manifestos, to the estimation of political positions from legislative speeches. Our “language-blind” word scoring technique successfully replicates published policy estimates without the substantial costs of time and labor that these require. Furthermore, unlike in any previous method for extracting policy positions from political texts, we provide uncertainty measures for our estimates, allowing analysts to make informed judgments of the extent to which differences between two estimated policy positions can be viewed as significant or merely as products of measurement error.

A Cross-modal Approach for Karaoke Artifacts Correction

Karaoke singing is a popular form of entertainment in several parts of the world. Since this genre of performance attracts amateurs, the singing often has artifacts related to scale, tempo, and synchrony. We have developed an approach to correct these artifacts using cross-modal multimedia streams information. We first perform adaptive sampling on the user's rendition and then use the original singer's rendition as well as the video caption highlighting information in order to correct the pitch, tempo and the loudness. A method of analogies has been employed to perform this correction. The basic idea is to manipulate the user's rendition in a manner to make it as similar as possible to the original singing. A pre-processing step of noise removal due to feedback and huffing also helps improve the quality of the user's audio. The results are described in the paper which shows the effectiveness of this multimedia approach.