Heritage language learning for Chinese Australians : the role of habitus

The relationship between Heritage Language and ethnic identity has gained significant research ground in social psychological and poststructural scholarship, with empirical evidence largely emerging from the North American settings. There is little pertinent sociological work conducted outside North America. To fill this gap, this sociological study sets its scene in an Australian context. Drawing on Bourdieu’s notion of habitus, the study examines the contribution of Chinese Australians’ Chineseness to their Chinese Heritage Language proficiency. Two hundred and thirty young Chinese Australians completed the online survey. Results from multiple regression indicate that habitus of Chineseness is one of the significant predictors for the Chinese Heritage Language proficiency of these young people. The study makes a theoretical contribution to investigate ethnic identity – Heritage Language link through the notion of habitus and makes a methodological contribution to quantify this habitus.

Quality based frame selection for face clustering in news video

Clustering identities in a broadcast video is a useful task to aid in video annotation and retrieval. Quality based frame selection is a crucial task in video face clustering, to both improve the clustering performance and reduce the computational cost. We present a frame work that selects the highest quality frames available in a video to cluster the face. This frame selection technique is based on low level and high level features (face symmetry, sharpness, contrast and brightness) to select the highest quality facial images available in a face sequence for clustering. We also consider the temporal distribution of the faces to ensure that selected faces are taken at times distributed throughout the sequence. Normalized feature scores are fused and frames with high quality scores are used in a Local Gabor Binary Pattern Histogram Sequence based face clustering system. We present a news video database to evaluate the clustering system performance. Experiments on the newly created news database show that the proposed method selects the best quality face images in the video sequence, resulting in improved clustering performance.

Image segmentation and pose estimation of humans in video

This thesis introduces improved techniques towards automatically estimating the pose of humans from video. It examines a complete workflow to estimating pose, from the segmentation of the raw video stream to extract silhouettes, to using the silhouettes in order to determine the relative orientation of parts of the human body. The proposed segmentation algorithms have improved performance and reduced complexity, while the pose estimation shows superior accuracy during difficult cases of self occlusion.

Patron-driven acquisition of online video

Letting the patron choose ebooks has been a successful experience. Why not apply the same purchase model to other formats? This showcase outlines Queensland University of Technology’s experience with a trial of patron driven acquisition (PDA) for online video. The trial commencing in August 2012 provided access to over 700 online videos licensed from Kanopy across a number of discipline areas. As online video publishing is still in the early stages of development, and as the trial is only in the very early stages, it is too early to draw any firm conclusions about the likely suitability of this model for online video selection and acquisition. However, the trial provides some interesting initial comparisons with ebook PDA and existing online video purchase models and prompts further consideration of PDA as a method for online video selection and licensing.

Possible, inevitable or fait accompli? An analysis of streaming video acquisition, acceptance and use in higher education

This paper explores the potential for online video as a mechanism to transform the ways students learn, as measured by research, user experience and usage following surveys and trials of patron-driven acquisition collaboratively undertaken by Queensland University of Technology, La Trobe University and Kanopy.

‘Teachers flipping out’ beyond the online lecture : maximising the educational potential of video

While video is recognised as an important medium for teaching and learning in the digital age, many video resources are not as effective as they might be, because they do not adequately exploit the strengths of the medium. Presented here are some case studies of video learning resources produced for various courses in a university environment. This ongoing project attempts to identify pedagogic strategies for the use of video; learning situations in which video has the most efficacy; and what production techniques can be employed to make effective video learning resources.

Semi-binary based video features for activity representation

Efficient and effective feature detection and representation is an important consideration when processing videos, and a large number of applications such as motion analysis, 3D scene understanding, tracking etc. depend on this. Amongst several feature description methods, local features are becoming increasingly popular for representing videos because of their simplicity and efficiency. While they achieve state-of-the-art performance with low computational complexity, their performance is still too limited for real world applications. Furthermore, rapid increases in the uptake of mobile devices has increased the demand for algorithms that can run with reduced memory and computational requirements. In this paper we propose a semi binary based feature detectordescriptor based on the BRISK detector, which can detect and represent videos with significantly reduced computational requirements, while achieving comparable performance to the state of the art spatio-temporal feature descriptors. First, the BRISK feature detector is applied on a frame by frame basis to detect interest points, then the detected key points are compared against consecutive frames for significant motion. Key points with significant motion are encoded with the BRISK descriptor in the spatial domain and Motion Boundary Histogram in the temporal domain. This descriptor is not only lightweight but also has lower memory requirements because of the binary nature of the BRISK descriptor, allowing the possibility of applications using hand held devices.We evaluate the combination of detectordescriptor performance in the context of action classification with a standard, popular bag-of-features with SVM framework. Experiments are carried out on two popular datasets with varying complexity and we demonstrate comparable performance with other descriptors with reduced computational complexity.

Acceptability-based QoE models for mobile video

Quality of experience (QoE) measures the overall perceived quality of mobile video delivery from subjective user experience and objective system performance. Current QoE computing models have two main limitations: 1) insufficient consideration of the factors influencing QoE, and; 2) limited studies on QoE models for acceptability prediction. In this paper, a set of novel acceptability-based QoE models, denoted as A-QoE, is proposed based on the results of comprehensive user studies on subjective quality acceptance assessments. The models are able to predict users’ acceptability and pleasantness in various mobile video usage scenarios. Statistical regression analysis has been used to build the models with a group of influencing factors as independent predictors, including encoding parameters and bitrate, video content characteristics, and mobile device display resolution. The performance of the proposed A-QoE models has been compared with three well-known objective Video Quality Assessment metrics: PSNR, SSIM and VQM. The proposed A-QoE models have high prediction accuracy and usage flexibility. Future user-centred mobile video delivery systems can benefit from applying the proposed QoE-based management to optimize video coding and quality delivery decisions.

Video analytics for the detection of near-miss incidents on approach to railway level crossings

Recent modelling of socio-economic costs by the Australian railway industry in 2010 has estimated the cost of level crossing accidents to exceed AU$116 million annually. To better understand causal factors that contribute to these accidents, the Cooperative Research Centre for Rail Innovation is running a project entitled Baseline Level Crossing Video. The project aims to improve the recording of level crossing safety data by developing an intelligent system capable of detecting near-miss incidents and capturing quantitative data around these incidents. To detect near-miss events at railway level crossings a video analytics module is being developed to analyse video footage obtained from forward-facing cameras installed on trains. This paper presents a vision base approach for the detection of these near-miss events. The video analytics module is comprised of object detectors and a rail detection algorithm, allowing the distance between a detected object and the rail to be determined. An existing publicly available Histograms of Oriented Gradients (HOG) based object detector algorithm is used to detect various types of vehicles in each video frame. As vehicles are usually seen from a sideway view from the cabin’s perspective, the results of the vehicle detector are verified using an algorithm that can detect the wheels of each detected vehicle. Rail detection is facilitated using a projective transformation of the video, such that the forward-facing view becomes a bird’s eye view. Line Segment Detector is employed as the feature extractor and a sliding window approach is developed to track a pair of rails. Localisation of the vehicles is done by projecting the results of the vehicle and rail detectors on the ground plane allowing the distance between the vehicle and rail to be calculated. The resultant vehicle positions and distance are logged to a database for further analysis. We present preliminary results regarding the performance of a prototype video analytics module on a data set of videos containing more than 30 different railway level crossings. The video data is captured from a journey of a train that has passed through these level crossings.

Evaluation of two-view geometry methods with automatic ground-truth generation

A large number of methods have been published that aim to evaluate various components of multi-view geometry systems. Most of these have focused on the feature extraction, description and matching stages (the visual front end), since geometry computation can be evaluated through simulation. Many data sets are constrained to small scale scenes or planar scenes that are not challenging to new algorithms, or require special equipment. This paper presents a method for automatically generating geometry ground truth and challenging test cases from high spatio-temporal resolution video. The objective of the system is to enable data collection at any physical scale, in any location and in various parts of the electromagnetic spectrum. The data generation process consists of collecting high resolution video, computing accurate sparse 3D reconstruction, video frame culling and down sampling, and test case selection. The evaluation process consists of applying a test 2-view geometry method to every test case and comparing the results to the ground truth. This system facilitates the evaluation of the whole geometry computation process or any part thereof against data compatible with a realistic application. A collection of example data sets and evaluations is included to demonstrate the range of applications of the proposed system.

Talk about a YouTube video in preschool : the mutual production of shared understanding for learning with digital technology

Much of what is written about digital technologies in preschool contexts focuses on young children’s acquisition of skills rather than their meaning-making during use of technologies. In this paper, we consider how the viewing of a YouTube video was used by a teacher and children to produce shared understandings about it. Conversation analysis of talk and interaction during the viewing of the video establishes some of the ways that individual accounts of events were produced for others and then endorsed as shared understandings. The analysis establishes how adults and children made use of verbal and embodied actions during interactions to produce shared understandings of the YouTube video, the events it recorded and written commentary about those events

Knowledge unlatched announces the launch of its pilot collection [Blog]

Frances Pinter and I have been visiting fellows at the Big Innovation Centre for more than a year now. Tucked away in a corner, inspired by BIC’s open innovation vision, we have been attempting to solve a problem that continues to perplex many in the era of digital affordance: creating sustainable markets for high quality new content that include free access for end users.

New directions for digital video creation in the classroom : spatiality, embodiment, and creativity

This column features a conversation (via email, image sharing, and Facetime) that took place over several months between two international theorists of digital filmmaking from schools in two countries—Professors Jason Ranker (Portland State University, Oregon, United States) and Kathy Mills (Queensland University of Technology, Australia). The authors discuss emerging ways of thinking about video making, sharing tips and anecdotes from classroom experience to inspire teachers to explore with adolescents the meaning potentials of digital video creation. The authors briefly discuss their previous work in this area, and then move into a discussion of how the material spaces in which students create videos profoundly shape the films' meanings and significance. The article ends with a discussion of how students can take up creative new directions, pushing the boundaries of the potentials of classroom video making and uncovering profound uses of the medium.

Cultural flows in an Aboriginal school : deterritorializing textual production through a socially mediated Indigenous heritage

Flows of cultural heritage in textual practices are vital to sustaining Indigenous communities. Indigenous heritage, whether passed on by oral tradition or ubiquitous social media, can be seen as a “conversation between the past and the future” (Fairclough, 2012, xv). Indigenous heritage involves appropriating memories within a cultural flow to pass on a spiritual legacy. This presentation reports ethnographic research of social media practices in a small independent Aboriginal school in Southeast Queensland, Australia that is resided over by the Yugambeh elders and an Aboriginal principal. The purpose was to rupture existing notions of white literacies in schools, and to deterritorialize the uses of digital media by dominant cultures in the public sphere. Examples of learning experiences included the following: i. Integrating Indigenous language and knowledge into media text production; ii. Using conversations with Indigenous elders and material artifacts as an entry point for storytelling; iii. Dadirri – spiritual listening in the yarning circle to develop storytelling (Ungunmerr-Baumann, 2002); and iv. Writing and publicly sharing oral histories through digital scrapbooking shared via social media. The program aligned with the Australian National Curriculum English (ACARA, 2012), which mandates the teaching of multimodal text creation. Data sources included a class set of digital scrapbooks collaboratively created in a multi-age primary classroom. The digital scrapbooks combined digitally encoded words, images of material artifacts, and digital music files. A key feature of the writing and digital design task was to retell and digitally display and archive a cultural narrative of significance to the Indigenous Australian community and its memories and material traces of the past for the future. Data analysis of the students’ digital stories involved the application of key themes of negotiated, material, and digitally mediated forms of heritage practice. It drew on Australian Indigenous research by Keddie et al. (2013) to guard against the homogenizing of culture that can arise from a focus on a static view of culture. The interpretation of findings located Indigenous appropriation of social media within broader racialized politics that enables Indigenous literacy to be understood as a dynamic, negotiated, and transgenerational flows of practice. The findings demonstrate that Indigenous children’s use of media production reflects “shifting and negotiated identities” in response to changing media environments that can function to sustain Indigenous cultural heritages (Appadurai, 1696, xv). It demonstrated how the children’s experiences of culture are layered over time, as successive generations inherit, interweave, and hear others’ cultural stories or maps. It also demonstrated how the children’s production of narratives through multimedia can provide a platform for the flow and reconstruction of performative collective memories and “lived traces of a common past” (Giaccardi, 2012). It disrupts notions of cultural reductionism and racial incommensurability that fix and homogenize Indigenous practices within and against a dominant White norm. Recommendations are provided for an approach to appropriating social media in schools that explicitly attends to the dynamic nature of Indigenous practices, negotiated through intercultural constructions and flows, and opening space for a critical anti-racist approach to multimodal text production.

Quantitative data collection and study validity

The quality of data collection methods selected and the integrity of the data collected are integral tot eh success of a study. This chapter focuses on data collection and study validity. After reading the chapter, readers should be able to define types of data collection methods in quantitative research; list advantages and disadvantages of each method; discuss factors related to internal and external validity; critically evaluate data collection methods and discuss the need to operationalise variables of interest for data collection.