Classification—Content Regulation and Convergent Media

On 24 March 2011, Attorney-General Robert McClelland referred the National Classification Scheme to the ALRC and asked it to conduct widespread public consultation across the community and industry. The review considered issues including: existing Commonwealth, State and Territory classification laws the current classification categories contained in the Classification Act, Code and Guidelines the rapid pace of technological change the need to improve classification information available to the community the effect of media on children and the desirability of a strong content and distribution industry in Australia. During the inquiry, the ALRC conducted face-to-face consultations with stakeholders, hosted two online discussion forums, and commissioned pilot community and reference group forums into community attitudes to higher level media content. The ALRC published two consultation documents—an Issues Paper and a Discussion Paper—and invited submissions from the public. The Final Report was tabled in Parliament on 1 March 2012. Recommendations: The report makes 57 recommendations for reform. The net effect of the recommendations would be the establishment of a new National Classification Scheme that: applies consistent rules to content that are sufficiently flexible to be adaptive to technological change; places a regulatory focus on restricting access to adult content, helping to promote cyber-safety and protect children from inappropriate content across media platforms; retains the Classification Board as an independent classification decision maker with an essential role in setting benchmarks; promotes industry co-regulation, encouraging greater industry content classification, with government regulation more directly focused on content of higher community concern; provides for pragmatic regulatory oversight, to meet community expectations and safeguard community standards; reduces the overall regulatory burden on media content industries while ensuring that content obligations are focused on what Australians most expect to be classified; and harmonises classification laws across Australia, for the benefit of consumers and content providers.

Academics and activists in the policy process : engagement with Australian media inquiries 2011-13

After its narrow re-election in June 2010, the Australian Labor government undertook a series of public inquiries into reform of Australian media, communications and copyright laws. One important driver of policy reform was the government’s commitment to building a National Broadband Network (NBN), and the implications this had for existing broadcasting and telecommunications policy, as it would constitute a major driver of convergence of media and communications access devices and content platforms. These inquiries included: the Convergence Review of media and communications legislation; the Australian Law Reform Commission (ALRC) review of the National Classification Scheme; the Independent Media Inquiry (Finkelstein Review) into Media and Media Regulation; and the ALRC review of Copyright and the Digital Economy. One unusual feature of this review process, discussed in the paper, was the degree to which academics were involved in the process, not simply as providers of expert opinion, but as review chairs seconded from their universities. This paper considers the role played by activist groups in all of these inquiries and their relationship to the various participants in the inquiries, as well as the implications of academics being engaged in such inquiries, not simply as activist-scholars, but as those primarily responsible for delivering policy review outcomes. The latter brings to the forefront issues arising in from direct engagement with governments and state agencies themselves, which challenges traditional understandings of the academic community as “critical outsiders” towards such policy processes.

Convergent media policy : the Australian case

This chapter considers the implications of convergence for media policy from three perspectives. First, it discusses what have been the traditional concerns of media policy, and the challenges it faces, from the perspectives of public interest theories, economic capture theories, and capitalist state theories. Second, it looks at what media convergence involves, and some of the dilemmas arising from convergent media policy including: (1) determining who is a media company; (2) regulatory parity between ‘old’ and ‘new’ media; (3) treatment of similar media content across different platforms; (4) distinguishing ‘big media’ from user-created content, and: (5) maintaining a distinction between media regulation and censorship of personal communication. Finally, it discusses attempts to reform media policy in light of these changes, including Australian media policy reports from 2011-12 including the Convergence Review, the Finkelstein Review of News Media, and the Australian Law Reform Commission’s National Classification Scheme Review. It concludes by arguing that ‘public interest’ approaches to media policy continue to have validity, even as they grapple with the complex question of how to understand the concept of influence in a convergent media environment.

Networked masculinities and social networking sites : a call for the analysis of men and contemporary digital media

It is of course recognised that technology can be gendered and implicated in gender relations. However, it continues to be the case that men’s experiences with technology are underexplored and the situation is even more problematic where digital media is concerned. Over the past 30 years we have witnessed a dramatic rise in the pervasiveness of digital media across many parts of the world and as associated with wide ranging aspects of our lives. This rise has been fuelled over the last decade by the emergence of Web 2.0 and particularly Social Networking Sites (SNS). Given this context, I believe it is necessary for us to undertake more work to understand men’s engagements with digital media, the implications this might have for masculinities and the analysis of gender relations more generally. To begin to unpack this area, I engage theorizations of the properties of digital media networks and integrate this with the masculinity studies field. Using this framework, I suggest we need to consider the rise in what I call networked masculinities – those masculinities (co)produced and reproduced with digitally networked publics. Through this analysis I discuss themes related to digital mediators, relationships, play and leisure, work and commerce, and ethics. I conclude that as masculinities can be, and are being, complicated and given agency by advancing notions and practices of connectivity, mobility, classification and convergence, those engaged with masculinity studies and digital media have much to contribute.

Academics in the policy process : engagement with Australian media policy inquiries 2011-2013

This paper critically evaluates the series of inquires that the Australian Labor government undertook during 2011-2013 into reform of Australian media, communications and copyright laws. One important driver of policy reform was the government’s commitment to building a National Broadband Network (NBN), and the implications this had for existing broadcasting and telecommunications policy, as it would constitute a major driver of convergence of media and communications access devices and content platforms. These inquiries included: the Convergence Review of media and communications legislation; the Australian Law Reform Commission (ALRC) review of the National Classification Scheme; and the Independent Media Inquiry (Finkelstein Review) into Media and Media Regulation. One unusual feature of this review process was the degree to which academics were involved in the process, not simply as providers of expert opinion, but as review chairs seconded from their universities. This paper considers the role played by activist groups in all of these inquiries and their relationship to the various participants in the inquiries, as well as the implications of academics being engaged in such inquiries, not simply as activist-scholars, but as those primarily responsible for delivering policy review outcomes. The paper draws upon the concept of "policy windows" in order to better understand the context in which the inquiries took place, and their relative lack of legislative impact.

A pilot study on affective classification of facial images for emerging news topics

The proliferation of news reports published in online websites and news information sharing among social media users necessitates effective techniques for analysing the image, text and video data related to news topics. This paper presents the first study to classify affective facial images on emerging news topics. The proposed system dynamically monitors and selects the current hot (of great interest) news topics with strong affective interestingness using textual keywords in news articles and social media discussions. Images from the selected hot topics are extracted and classified into three categorized emotions, positive, neutral and negative, based on facial expressions of subjects in the images. Performance evaluations on two facial image datasets collected from real-world resources demonstrate the applicability and effectiveness of the proposed system in affective classification of facial images in news reports. Facial expression shows high consistency with the affective textual content in news reports for positive emotion, while only low correlation has been observed for neutral and negative. The system can be directly used for applications, such as assisting editors in choosing photos with a proper affective semantic for a certain topic during news report preparation.

Convergent Media Policy

This chapter considers the implications of convergence for media policy from three perspectives. First, it discusses what have been the traditional concerns of media policy, and the challenges it faces, from the perspectives of public interest theories, economic capture theories, and capitalist state theories. Second, it looks at what media convergence involves, and some of the dilemmas arising from convergent media policy including: (1) determining who is a media company; (2) regulatory parity between ‘old’ and ‘new’ media; (3) treatment of similar media content across different platforms; (4) distinguishing ‘big media’ from user-created content; and (5) maintaining a distinction between media regulation and censorship of personal communication. Finally, it discusses attempts to reform media policy in light of these changes, including Australian media policy reports from 2011-12 including the Convergence Review, the Finkelstein Review of News Media, and the Australian Law Reform Commission’s National Classification Scheme Review. It concludes by arguing that ‘public interest’ approaches to media policy continue to have validity, even as they grapple with the complex question of how to understand the concept of influence in a convergent media environment.

Evaluation of features for leaf classification in challenging conditions

Fine-grained leaf classification has concentrated on the use of traditional shape and statistical features to classify ideal images. In this paper we evaluate the effectiveness of traditional hand-crafted features and propose the use of deep convolutional neural network (ConvNet) features. We introduce a range of condition variations to explore the robustness of these features, including: translation, scaling, rotation, shading and occlusion. Evaluations on the Flavia dataset demonstrate that in ideal imaging conditions, combining traditional and ConvNet features yields state-of-theart performance with an average accuracy of 97:3%�0:6% compared to traditional features which obtain an average accuracy of 91:2%�1:6%. Further experiments show that this combined classification approach consistently outperforms the best set of traditional features by an average of 5:7% for all of the evaluated condition variations.

Towards robust automatic affective classification of images using facial expressions for practical applications

Affect is an important feature of multimedia content and conveys valuable information for multimedia indexing and retrieval. Most existing studies for affective content analysis are limited to low-level features or mid-level representations, and are generally criticized for their incapacity to address the gap between low-level features and high-level human affective perception. The facial expressions of subjects in images carry important semantic information that can substantially influence human affective perception, but have been seldom investigated for affective classification of facial images towards practical applications. This paper presents an automatic image emotion detector (IED) for affective classification of practical (or non-laboratory) data using facial expressions, where a lot of “real-world” challenges are present, including pose, illumination, and size variations etc. The proposed method is novel, with its framework designed specifically to overcome these challenges using multi-view versions of face and fiducial point detectors, and a combination of point-based texture and geometry. Performance comparisons of several key parameters of relevant algorithms are conducted to explore the optimum parameters for high accuracy and fast computation speed. A comprehensive set of experiments with existing and new datasets, shows that the method is effective despite pose variations, fast, and appropriate for large-scale data, and as accurate as the method with state-of-the-art performance on laboratory-based data. The proposed method was also applied to affective classification of images from the British Broadcast Corporation (BBC) in a task typical for a practical application providing some valuable insights.

Info-active and info-passive: The new type of consumer classification

Social media platforms, that foster user generated content, have altered the ways consumers search for product related information. Conducting online searches, reading product reviews, and comparing products ratings, is becoming a more common information seeking pathway. This research demonstrates that info-active consumers are becoming less reliant on information provided by retailers or manufacturers, hence marketing generated online content may have a reduced impact on their purchasing behaviour. The results of this study indicate that beyond traditional methods of segmenting consumers, in the online context, new classifications such as info-active and info-passive would be beneficial in digital marketing. This cross-sectional, mixed-methods study is based on 43 in-depth interviews and an online survey with 500 consumers from 30 countries.

Acoustic classification of Australian anurans using syllable features

Acoustic classification of anurans (frogs) has received increasing attention for its promising application in biological and environment studies. In this study, a novel feature extraction method for frog call classification is presented based on the analysis of spectrograms. The frog calls are first automatically segmented into syllables. Then, spectral peak tracks are extracted to separate desired signal (frog calls) from background noise. The spectral peak tracks are used to extract various syllable features, including: syllable duration, dominant frequency, oscillation rate, frequency modulation, and energy modulation. Finally, a k-nearest neighbor classifier is used for classifying frog calls based on the results of principal component analysis. The experiment results show that syllable features can achieve an average classification accuracy of 90.5% which outperforms Mel-frequency cepstral coefficients features (79.0%).

Acoustic feature extraction using perceptual wavelet packet decomposition for frog call classification

Frog protection has become increasingly essential due to the rapid decline of its biodiversity. Therefore, it is valuable to develop new methods for studying this biodiversity. In this paper, a novel feature extraction method is proposed based on perceptual wavelet packet decomposition for classifying frog calls in noisy environments. Pre-processing and syllable segmentation are first applied to the frog call. Then, a spectral peak track is extracted from each syllable if possible. Track duration, dominant frequency and oscillation rate are directly extracted from the track. With k-means clustering algorithm, the calculated dominant frequency of all frog species is clustered into k parts, which produce a frequency scale for wavelet packet decomposition. Based on the adaptive frequency scale, wavelet packet decomposition is applied to the frog calls. Using the wavelet packet decomposition coefficients, a new feature set named perceptual wavelet packet decomposition sub-band cepstral coefficients is extracted. Finally, a k-nearest neighbour (k-NN) classifier is used for the classification. The experiment results show that the proposed features can achieve an average classification accuracy of 97.45% which outperforms syllable features (86.87%) and Mel-frequency cepstral coefficients (MFCCs) feature (90.80%).

Application of image processing techniques for frog call classification

Frogs have received increasing attention due to their effectiveness for indicating the environment change. Therefore, it is important to monitor and assess frogs. With the development of sensor techniques, large volumes of audio data (including frog calls) have been collected and need to be analysed. After transforming the audio data into its spectrogram representation using short-time Fourier transform, the visual inspection of this representation motivates us to use image processing techniques for analysing audio data. Applying acoustic event detection (AED) method to spectrograms, acoustic events are firstly detected from which ridges are extracted. Three feature sets, Mel-frequency cepstral coefficients (MFCCs), AED feature set and ridge feature set, are then used for frog call classification with a support vector machine classifier. Fifteen frog species widely spread in Queensland, Australia, are selected to evaluate the proposed method. The experimental results show that ridge feature set can achieve an average classification accuracy of 74.73% which outperforms the MFCCs (38.99%) and AED feature set (67.78%).

Acoustic features for multi-level classification of Australian frogs

Over past few decades, frog species have been experiencing dramatic decline around the world. The reason for this decline includes habitat loss, invasive species, climate change and so on. To better know the status of frog species, classifying frogs has become increasingly important. In this study, acoustic features are investigated for multi-level classification of Australian frogs: family, genus and species, including three families, eleven genera and eighty five species which are collected from Queensland, Australia. For each frog species, six instances are selected from which ten acoustic features are calculated. Then, the multicollinearity between ten features are studied for selecting non-correlated features for subsequent analysis. A decision tree (DT) classifier is used to visually and explicitly determine which acoustic features are relatively important for classifying family, which for genus, and which for species. Finally, a weighted support vector machines (SVMs) classifier is used for the multi- level classification with three most important acoustic features respectively. Our experiment results indicate that using different acoustic feature sets can successfully classify frogs at different levels and the average classification accuracy can be up to 85.6%, 86.1% and 56.2% for family, genus and species respectively.