The micro-movie wave in a globalising China : adaptation, formalisation and commercialisation

While user-generated short online videos have existed since the emergence of video sharing sites in China, they have undergone a process of formalisation and commercialisation, culminating in the wave of micro-movies in recent years. By addressing the wider context of globalisation alongside relevant state policies and shifting viewing habits, this article analyses the local and global causes of this wave. It offers evidence that illustrates how online video service providers in China have adapted in a changing industry landscape as they negotiate state policies, advertiser interests and user preference. It then examines the production and distribution dynamics, where professional producers draw on social media, grassroots creativity and creative talents in regional markets. Finally, it discusses the cultural implications of this process in terms of both the nature and flow of creativity. Based on these analyses, the article also sheds light on the interplay between the state and the market in the context of globalisation and marketisation of media sectors, which becomes more complicated when the state-owned or controlled media enter the emerging market sectors.

The remediation of the personal photograph and the politics of self-representation in digital storytelling

Over the past couple of decades, the cultural field formerly known as ‘domestic’, and later ‘personal’ photography has been remediated and transformed as part of the social web, with its convergence of personal expression, interpersonal communication, and online social networks (most recently via platforms like Flickr, Facebook and Twitter). Meanwhile, the Digital Storytelling movement (involving the workshop-based production of short autobiographical videos) from its beginnings in the mid 1990s relied heavily on the narrative power of the personal photograph, often sourced from family albums, and later from online archives. This paper addresses the new issues arising for the politics of self-representation and personal photography in the era of social media, focusing particularly on the consequences of online image-sharing. It discusses in detail the practices of selection, curation, manipulation and editing of personal photographic images among a group of activist-oriented queer digital storytellers who have in common a stated desire to share their personal stories in pursuit of social change, and whose stories often aim to address both intimate and antagonistic publics.

Integrating collaborative filtering and matching-based search for product recommendations

Currently, recommender systems (RS) have been widely applied in many commercial e-commerce sites to help users deal with the information overload problem. Recommender systems provide personalized recommendations to users and, thus, help in making good decisions about which product to buy from the vast amount of product choices. Many of the current recommender systems are developed for simple and frequently purchased products like books and videos, by using collaborative-filtering and content-based approaches. These approaches are not directly applicable for recommending infrequently purchased products such as cars and houses as it is difficult to collect a large number of ratings data from users for such products. Many of the ecommerce sites for infrequently purchased products are still using basic search-based techniques whereby the products that match with the attributes given in the target user’s query are retrieved and recommended. However, search-based recommenders cannot provide personalized recommendations. For different users, the recommendations will be the same if they provide the same query regardless of any difference in their interest. In this article, a simple user profiling approach is proposed to generate user’s preferences to product attributes (i.e., user profiles) based on user product click stream data. The user profiles can be used to find similarminded users (i.e., neighbours) accurately. Two recommendation approaches are proposed, namely Round- Robin fusion algorithm (CFRRobin) and Collaborative Filtering-based Aggregated Query algorithm (CFAgQuery), to generate personalized recommendations based on the user profiles. Instead of using the target user’s query to search for products as normal search based systems do, the CFRRobin technique uses the attributes of the products in which the target user’s neighbours have shown interest as queries to retrieve relevant products, and then recommends to the target user a list of products by merging and ranking the returned products using the Round Robin method. The CFAgQuery technique uses the attributes of the products that the user’s neighbours have shown interest in to derive an aggregated query, which is then used to retrieve products to recommend to the target user. Experiments conducted on a real e-commerce dataset show that both the proposed techniques CFRRobin and CFAgQuery perform better than the standard Collaborative Filtering and the Basic Search approaches, which are widely applied by the current e-commerce applications.

How digital technologies have changed the way we “teach”

Proud suggested that the biggest and most obvious impact of the digital world felt by academics, was in the area of teaching. He demonstrated a number of the initiatives which have been by developed by outside organizations and within various universities. Those include larger classrooms, online teaching and Blackboard. All of these were believed to provide improved learning by students, but, most commonly also expanded the faculty workload. He then discussed a number of the newer technologies which are becoming available such as the virtual classroom, Google Glass, Adobe online, Skype and others. All of these tools, he argued were in response to increasing economic pressures on the University, the result of which is that entire courses have migrated online. The reason for university interest in these new technologies were listed as reduced need for classrooms and classroom space, less need for on-campus facilities and even a decline in need for weekly in-class lectures. Thus, it has been argued that these new tools and technologies liberate the faculty from the tyranny of geography through the introduction of blogs, online videos, discussion forums and communication tools such as wikis, Facebook sites and Yammer, all of which seem to have specific advantages. The question raised, however, is: How successful have these new digital innovations been? As an example, he cited his own experience in teaching distance learning programs in Thailand and elsewhere. Those results are still being reviewed, with no definitive view developed.

An evaluation of different features and learning models for anomalous event detection

The huge amount of CCTV footage available makes it very burdensome to process these videos manually through human operators. This has made automated processing of video footage through computer vision technologies necessary. During the past several years, there has been a large effort to detect abnormal activities through computer vision techniques. Typically, the problem is formulated as a novelty detection task where the system is trained on normal data and is required to detect events which do not fit the learned ‘normal’ model. There is no precise and exact definition for an abnormal activity; it is dependent on the context of the scene. Hence there is a requirement for different feature sets to detect different kinds of abnormal activities. In this work we evaluate the performance of different state of the art features to detect the presence of the abnormal objects in the scene. These include optical flow vectors to detect motion related anomalies, textures of optical flow and image textures to detect the presence of abnormal objects. These extracted features in different combinations are modeled using different state of the art models such as Gaussian mixture model(GMM) and Semi- 2D Hidden Markov model(HMM) to analyse the performances. Further we apply perspective normalization to the extracted features to compensate for perspective distortion due to the distance between the camera and objects of consideration. The proposed approach is evaluated using the publicly available UCSD datasets and we demonstrate improved performance compared to other state of the art methods.

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.

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.

Swimmer localization from a moving camera

At the highest level of competitive sport, nearly all performances of athletes (both training and competitive) are chronicled using video. Video is then often viewed by expert coaches/analysts who then manually label important performance indicators to gauge performance. Stroke-rate and pacing are important performance measures in swimming, and these are previously digitised manually by a human. This is problematic as annotating large volumes of video can be costly, and time-consuming. Further, since it is difficult to accurately estimate the position of the swimmer at each frame, measures such as stroke rate are generally aggregated over an entire swimming lap. Vision-based techniques which can automatically, objectively and reliably track the swimmer and their location can potentially solve these issues and allow for large-scale analysis of a swimmer across many videos. However, the aquatic environment is challenging due to fluctuations in scene from splashes, reflections and because swimmers are frequently submerged at different points in a race. In this paper, we temporally segment races into distinct and sequential states, and propose a multimodal approach which employs individual detectors tuned to each race state. Our approach allows the swimmer to be located and tracked smoothly in each frame despite a diverse range of constraints. We test our approach on a video dataset compiled at the 2012 Australian Short Course Swimming Championships.

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.

Development of an integrated GIS and land use planning course : impacts of hybrid instructional methods

This study reports an action research undertaken at Queensland University of Technology. It evaluates the effectiveness of the integration of GIS within the substantive domains of an existing land use planning course in 2011. Using student performance, learning experience survey, and questionnaire survey data, it also evaluates the impacts of incorporating hybrid instructional methods (e.g., in-class and online instructional videos) in 2012 and 2013. Results show that: students (re)iterated the importance of GIS in the course justifying the integration; the hybrid methods significantly increased student performance; and unlike replacement, the videos are more suitable as a complement to in-class activity.

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.

Bucket attack on numeric set watermarking model and safeguards

Numeric set watermarking is a way to provide ownership proof for numerical data. Numerical data can be considered to be primitives for multimedia types such as images and videos since they are organized forms of numeric information. Thereby, the capability to watermark numerical data directly implies the capability to watermark multimedia objects and discourage information theft on social networking sites and the Internet in general. Unfortunately, there has been very limited research done in the field of numeric set watermarking due to underlying limitations in terms of number of items in the set and LSBs in each item available for watermarking. In 2009, Gupta et al. proposed a numeric set watermarking model that embeds watermark bits in the items of the set based on a hash value of the items’ most significant bits (MSBs). If an item is chosen for watermarking, a watermark bit is embedded in the least significant bits, and the replaced bit is inserted in the fractional value to provide reversibility. The authors show their scheme to be resilient against the traditional subset addition, deletion, and modification attacks as well as secondary watermarking attacks. In this paper, we present a bucket attack on this watermarking model. The attack consists of creating buckets of items with the same MSBs and determine if the items of the bucket carry watermark bits. Experimental results show that the bucket attack is very strong and destroys the entire watermark with close to 100% success rate. We examine the inherent weaknesses in the watermarking model of Gupta et al. that leave it vulnerable to the bucket attack and propose potential safeguards that can provide resilience against this attack.

Producing while consuming: Social interaction around photos shared within private group

User-generated content plays a pivotal role in the current social media. The main focus, however, has been on the explicitly generated user content such as photos, videos and status updates on different social networking sites. In this paper, we explore the potential of implicitly generated user content, based on users’ online consumption behaviors. It is technically feasible to record users’ consumption behaviors on mobile devices and share that with relevant people. Mobile devices with such capabilities could enrich social interactions around the consumed content, but it may also threaten users’ privacy. To understand the potentials of this design direction we created and evaluated a low-fidelity prototype intended for photo sharing within private groups. Our prototype incorporates two design concepts, namely, FingerPrint and MoodPhotos that leverage users’ consumption history and emotional responses. In this paper, we report user values and user acceptance of this prototype from three participatory design workshops.

Exploring community building with an awareness display

In this paper, we present a field trial of a pervasive system called Panorama that is aimed at supporting social awareness in work environments. Panorama is an intelligent situated display in the staff room of an academic department. It artistically represents non-critical user generated content such as images from holidays, conferences and other social gatherings, as well as textual messages on its display. It also captures images and videos from different public spaces of the department and streams them onto the Panorama screen, using appropriate abstraction techniques. We studied the use of Panorama for two weeks and observed how Panorama affected staff members' social awareness and community building. We report that Panorama simulated curiosity and learning, initiated new interactions and provided a mechanism for cherishing old memories.

Tell me what you see : pre-service teachers' recognition of exemplary digital pedagogy

How do you identify "good" teaching practice in the complexity of a real classroom? How do you know that beginning teachers can recognise effective digital pedagogy when they see it? How can teacher educators see through their students’ eyes? The study in this paper has arisen from our interest in what pre-service teachers “see” when observing effective classroom practice and how this might reveal their own technological, pedagogical and content knowledge. We asked 104 pre-service teachers from Early Years, Primary and Secondary cohorts to watch and comment upon selected exemplary videos of teachers using ICT (information and communication technologies) in Science. The pre-service teachers recorded their observations using a simple PMI (plus, minus, interesting) matrix which were then coded using the SOLO Taxonomy to look for evidence of their familiarity with and judgements of digital pedagogies. From this, we determined that the majority of preservice teachers we surveyed were using a descriptive rather than a reflective strategy, that is, not extending beyond what was demonstrated in the teaching exemplar or differentiating between action and purpose. We also determined that this method warrants wider trialling as a means of evaluating students’ understandings of the complexity of the digital classroom.