Entrapping Christian and Muslim Arabs in racial cartoons in Australia : the other anti-semitism

Racial Cartoons are a powerful force disguised as entertainment operating to shape public opinion. During the 1980s, 1990s and after 9/11 in 2001, cartoons in the Australian press were particularly directed against Muslim and Christian Arabs without remorse or fear of redress or accountability. The offensive of such cartoons has essentially been directed on three fronts—oil, politics and religion. The drawback resulting from socio-cultural, historical and other differences are no doubt visible; but equally obvious is that anti-Semitism, which was directed against the Jews in the 1930s and 1940s, is today mostly directed against the public relations deprived, opinion silenced and undemocratically governed, ethnically diverse Arabs. It is argued in this paper that several forces were behind such distorted visual strategies adopted by the Australian press. Pre-judgement stemming from an inbuilt bias of the cartoonist, or highlighting characteristics which conform to the national interest are likely factors. The debate in Australia as to whether public images and attitudes of a minority “cause” or “determine” policy or whether policy itself changes attitudes is still resting with the jury.

Poetry and public speech: three traces

Poetry is routinely seen as ‘marginal’ to public culture, especially in terms of it having lost its status as a form of public speech. Such a condition is often noted in nostalgic terms, in which a golden era—bardic or journalistic—is evoked to illustrate contemporary poetry’s lack. But traces of poetry’s instrumentality, especially as a form of public speech, can be found in various extra-poetic contexts.

In this article, three examples of poetry operating in ‘extra-poetic contexts’ will illustrate the different, sometimes troubling, ways in which traces of poetry as a mode of public speech can be observed in contemporary culture: the poem-cartoons of Michael Leunig; the role of the poet Les Murray in the drafting of a proposed preamble to the Constitution of Australia; and the quotation of William Ernest Henley’s ‘Invictus’ as the final statement of Timothy McVeigh (the ‘Ohio Bomber’) prior to his execution.

These examples illustrate that poetry-as-public-speech engages with political discourse in diverse, incommensurate ways. Leunig’s occasional cartoon-poems, appearing in the metropolitan press, are examples of poetry at its most public and politically engaged state. And yet, even Leunig’s most ‘political’ work gestures towards a realm beyond politics, where the poetic, the comic, and the existential coexist as a way of making life in the political realm more bearable. Les Murray’s role as a ‘national’ poet in the failed attempt to introduce a preamble to the Australian Constitution illustrates the vestigial role that poets can play in nation building. Lastly, McVeigh’s quotation of Henley, made without any explanation, shows the unpredictable and potentially volatile condition of poetry-as-public-speech. In addition, the examples variously engage in arguments about the relationship between the individual and the state, private identity and national history.

Automatic genre identification for content-based video categorization

This paper presents a set of computational features originating from our study of editing effects, motion, and color used in videos, for the task of automatic video categorization. These features besides representing human understanding of typical attributes of different video genres, are also inspired by the techniques and rules used by many directors to endow specific characteristics to a genre-program which lead to certain emotional impact on viewers. We propose new features whilst also employing traditionally used ones for classification. This research, goes beyond the existing work with a systematic analysis of trends exhibited by each of our features in genres such as cartoons, commercials, music, news, and sports, and it enables an understanding of the similarities, dissimilarities, and also likely confusion between genres. Classification results from our experiments on several hours of video establish the usefulness of this feature set. We also explore the issue of video clip duration required to achieve reliable genre identification and demonstrate its impact on classification accuracy.

Video genre categorization using audio wavelet coefficients

In this paper, we investigate the use of a wavelet transform-based analysis of audio tracks accompanying videos for the problem of automatic program genre detection. We compare the classification performance based on wavelet-based audio features to that using conventional features derived from Fourier and time analysis for the task of discriminating TV programs such as news, commercials, music shows, concerts, motor racing games, and animated cartoons. Three different classifiers namely the Decision Trees, SVMs, and k-Nearest Neighbours are studied to analyse the reliability of the performance of our wavelet features based approach. Further, we investigate the issue of an appropriate duration of an audio clip to be analyzed for this automatic genre determination. Our experimental results show that features derived from the wavelet transform of the audio signal can very well separate the six video genres studied. It is also found that there is no significant difference in performance with varying audio clip durations across the classifiers.