Critical literacy as a pedagogical goal in English language teaching

In this chapter, the authors provide an overview of the area of critical literacy as it pertains to second language pedagogy (curriculum and instruction). After considering the historical origins of critical literacy (from antiquity, and including in first language education), they consider how it began to penetrate the field of applied linguistics. They note the geographical and institutional spread of critical literacy practice as documented by published accounts. They then sketch the main features of L2 critical literacy practice. To do this, they acknowledge how practitioners have reported on their practices regarding classroom content and process. The authors also draw attention to the outcomes of these practices as well as challenges that practitioners have encountered in incorporating critical literacy into their second language classrooms

What's Making that Sound?

In this paper, we investigate techniques to localize the sound source in video made using one microphone. The visual object whose motion generates the sound is located and segmented based on the synchronization analysis of object motion and audio energy. We first apply an effective region tracking algorithm to segment the video into a number of spatial-temporal region tracks, each representing the temporal evolution of an appearance-coherent image structure (i.e., object). We then extract the motion features of each object as its average acceleration in each frame. Meanwhile, Short-term Fourier Transform is applied to the audio signal to extract audio energy feature as the audio descriptor. We further impose a nonlinear transformation on both audio and visual descriptors to obtain the audio and visual codes in a common rank correlation space. Finally, the correlation between an object and the audio signal is simply evaluated by computing the Hamming distance between the audio and visual codes generated in previous steps. We evaluate the proposed method both qualitatively and quantitatively using a number of challenging test videos. In particular, the proposed method is compared with a state-of-the-art audiovisual source localization algorithm. The results demonstrate the superior performance of the proposed algorithm in spatial-temporal localization and segmentation of audio sources in the visual domain

Performance measures and a data set for multi-target, multi-camera tracking

To help accelerate progress in multi-target, multi-camera tracking systems, we present (i) a new pair of precision-recall measures of performance that treats errors of all types uniformly and emphasizes correct identification over sources of error; (ii) the largest fully-annotated and calibrated data set to date with more than 2 million frames of 1080 p, 60 fps video taken by 8 cameras observing more than 2, 700 identities over 85 min; and (iii) a reference software system as a comparison baseline. We show that (i) our measures properly account for bottom-line identity match performance in the multi-camera setting; (ii) our data set poses realistic challenges to current trackers; and (iii) the performance of our system is comparable to the state of the art