Syria's Arab Spring : language enrichment in the midst of revolution

This paper analyzes linguistic transformations of the Arabic language that have been taking place since the start of the revolution in March 2011. Building on Basil Bernstein‟s sociolinguistic theory of language codes (2002), the paper starts by providing an analysis of the communication codes developed by several Syrian communities (in Damascus, Homs, and Hama) since the 1970s. In doing so, the paper argues that restricted codes were used by individuals across social classes and religious communities in the face of an oppressing regime. The paper then moves to the examination of the current impact of the political demonstrations and activism on the Arabic language in Syria, and argues that four significant changes are noticeable: a creativity process through which new words have been formed, while other existing words have undergone semantic changes (using Laurie Bauer‟s theory of naming needs); the rise of popular Syrian slogans adopted and quoted in the Arabic media in their dialectal form; a battle of words taking place between the anti-Assad demonstrators and the pro-Assad counterparts; and a symbolic use of language to show the unity of the Syrian people. These findings demonstrate both the changing nature of the Arabic language and the significant impact conflict situations may carry upon it. While these findings apply to Syria and its particular case in the Arab Spring, they may advance sociolinguistic studies of language creativity in zones of political repression and conflict

Syria's Arab Spring : language enrichment in the midst of revolution

This paper analyzes linguistic transformations of the Arabic language that have been taking place since the start of the revolution in March 2011. Building on Basil Bernstein‟s sociolinguistic theory of language codes (2002), the paper starts by providing an analysis of the communication codes developed by several Syrian communities (in Damascus, Homs, and Hama) since the 1970s. In doing so, the paper argues that restricted codes were used by individuals across social classes and religious communities in the face of an oppressing regime. The paper then moves to the examination of the current impact of the political demonstrations and activism on the Arabic language in Syria, and argues that four significant changes are noticeable: a creativity process through which new words have been formed, while other existing words have undergone semantic changes (using Laurie Bauer‟s theory of naming needs); the rise of popular Syrian slogans adopted and quoted in the Arabic media in their dialectal form; a battle of words taking place between the anti-Assad demonstrators and the pro-Assad counterparts; and a symbolic use of language to show the unity of the Syrian people. These findings demonstrate both the changing nature of the Arabic language and the significant impact conflict situations may carry upon it. While these findings apply to Syria and its particular case in the Arab Spring, they may advance sociolinguistic studies of language creativity in zones of political repression and conflict

Thermal spatio-temporal data for stress recognition

Stress is a serious concern facing our world today, motivating the development of a better objective understanding through the use of non-intrusive means for stress recognition by reducing restrictions to natural human behavior. As an initial step in computer vision-based stress detection, this paper proposes a temporal thermal spectrum (TS) and visible spectrum (VS) video database ANUStressDB - a major contribution to stress research. The database contains videos of 35 subjects watching stressed and not-stressed film clips validated by the subjects. We present the experiment and the process conducted to acquire videos of subjects' faces while they watched the films for the ANUStressDB. Further, a baseline model based on computing local binary patterns on three orthogonal planes (LBP-TOP) descriptor on VS and TS videos for stress detection is presented. A LBP-TOP-inspired descriptor was used to capture dynamic thermal patterns in histograms (HDTP) which exploited spatio-temporal characteristics in TS videos. Support vector machines were used for our stress detection model. A genetic algorithm was used to select salient facial block divisions for stress classification and to determine whether certain regions of the face of subjects showed better stress patterns. Results showed that a fusion of facial patterns from VS and TS videos produced statistically significantly better stress recognition rates than patterns from VS or TS videos used in isolation. Moreover, the genetic algorithm selection method led to statistically significantly better stress detection rates than classifiers that used all the facial block divisions. In addition, the best stress recognition rate was obtained from HDTP features fused with LBP-TOP features for TS and VS videos using a hybrid of a genetic algorithm and a support vector machine stress detection model. The model produced an accuracy of 86%