Capturing the 'authentic voice' : challenges and opportunities for voice and self-representation in two ABC storytelling projects

In recent years there has been a noticeable move by various public institutions, such as public service broadcasters and community media organisations, to capture and disseminate the voices and viewpoints of ‘ordinary people’ through inviting them to share stories about their lives. One of the foremost objectives of many such projects is to provide under-represented individuals and groups with an opportunity to express and represent themselves; as such, the capture and broadcast of ‘authentic voices’ is a central value. This paper discusses the notion of ‘authentic voice’, and questions the framing role of public media organisations in storytelling projects that aim to provide individuals with space for self-expression and self-representation. It considers the ways in which tensions arise on multiple levels when individuals are asked to express and represent themselves within projects and spaces that are managed by institutions. This paper begins by discussing the challenges and opportunities that arise within storytelling projects that are facilitated by public institutions and community media arts organisations, and that aim to amplify the voices of “ordinary people” (Thumim, 2009). It examines ways in which ‘voice’ is facilitated, curated, broadcast and distributed within such projects, particularly questioning the ways in which project facilitation and the curation of stories for public broadcast can both help and hinder the amplification of ‘authentic voice’. Furthermore, we seek to discuss how ‘authentic voice’ is defined, and what is involved in the process of amplification. The paper moves on to discuss a case study in order to demonstrate some of the tensions that are evident within a storytelling project that is managed by a public institution – Australia’s national broadcaster – and the ways these tensions impact upon the capture and broadcast of an ‘authentic voice’ for project participants. The Australian Broadcasting Corporation’s (ABC) ‘Heywire’ project is a storytelling competition and website that aims to ‘give voice’ to 16-22 year olds who live in rural, regional and remote parts of Australia. Looking at tensions that exist on organisational, political and philosophical levels within the Heywire project reveals a number of conflicts of interest and objectives between the institution and project participants. This leads us to question whether institutionally-managed storytelling projects can effectively support individuals to have an ‘authentic voice’, and whether struggles of aims and objectives diminish the personal benefits that people may derive from expressing and representing themselves within such projects.

A novel T-DNA vector design for selection of transgenic lines with simple transgene integration and stable transgene expression

Plants transformed with Agrobacterium frequently contain T-DNA concatamers with direct-repeat (d/r) or inverted-repeat (i/r) transgene integrations, and these repetitive T-DNA insertions are often associated with transgene silencing. To facilitate the selection of transgenic lines with simple T-DNA insertions, we constructed a binary vector (pSIV) based on the principle of hairpin RNA (hpRNA)-induced gene silencing. The vector is designed so that any transformed cells that contain more than one insertion per locus should generate hpRNA against the selective marker gene, leading to its silencing. These cells should, therefore, be sensitive to the selective agent and less likely to regenerate. Results from Arabidopsis and tobacco transformation showed that pSIV gave considerably fewer transgenic lines with repetitive insertions than did a conventional T-DNA vector (pCON). Furthermore, the transgene was more stably expressed in the pSIV plants than in the pCON plants. Rescue of plant DNA flanking sequences from pSIV plants was significantly more frequent than from pCON plants, suggesting that pSIV is potentially useful for T-DNA tagging. Our results revealed a perfect correlation between the presence of tail-to-tail inverted repeats and transgene silencing, supporting the view that read-through hpRNA transcript derived from i/r T-DNA insertions is a primary inducer of transgene silencing in plants. © CSIRO 2005.

Marker gene elimination from transgenic barley, using co-transformation with adjacent 'twin T-DNAs' on a standard Agrobacterium transformation vector

We have tested a methodology for the elimination of the selectable marker gene after Agrobacterium-mediated transformation of barley. This involves segregation of the selectable marker gene away from the gene of interest following co-transformation using a plasmid carrying two T-DNAs, which were located adjacent to each other with no intervening region. A standard binary transformation vector was modified by insertion of a small section composed of an additional left and right T-DNA border, so that the selectable marker gene and the site for insertion of the gene of interest (GOI) were each flanked by a left and right border. Using this vector three different GOIs were transformed into barley. Analysis of transgene inheritance was facilitated by a novel and rapid assay utilizing PCR amplification from macerated leaf tissue. Co-insertion was observed in two thirds of transformants, and among these approximately one quarter had transgene inserts which segregated in the next generation to yield selectable marker-free transgenic plants. Insertion of non-T-DNA plasmid sequences was observed in only one of fourteen SMF lines tested. This technique thus provides a workable system for generating transgenic barley free from selectable marker genes, thereby obviating public concerns regarding proliferation of these genes.

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.

Improving short utterance i-vector speaker verification using utterance variance modelling and compensation techniques

This paper proposes techniques to improve the performance of i-vector based speaker verification systems when only short utterances are available. Short-length utterance i-vectors vary with speaker, session variations, and the phonetic content of the utterance. Well established methods such as linear discriminant analysis (LDA), source-normalized LDA (SN-LDA) and within-class covariance normalisation (WCCN) exist for compensating the session variation but we have identified the variability introduced by phonetic content due to utterance variation as an additional source of degradation when short-duration utterances are used. To compensate for utterance variations in short i-vector speaker verification systems using cosine similarity scoring (CSS), we have introduced a short utterance variance normalization (SUVN) technique and a short utterance variance (SUV) modelling approach at the i-vector feature level. A combination of SUVN with LDA and SN-LDA is proposed to compensate the session and utterance variations and is shown to provide improvement in performance over the traditional approach of using LDA and/or SN-LDA followed by WCCN. An alternative approach is also introduced using probabilistic linear discriminant analysis (PLDA) approach to directly model the SUV. The combination of SUVN, LDA and SN-LDA followed by SUV PLDA modelling provides an improvement over the baseline PLDA approach. We also show that for this combination of techniques, the utterance variation information needs to be artificially added to full-length i-vectors for PLDA modelling.

Predicting fault-prone software modules with rank sum classification

The detection and correction of defects remains among the most time consuming and expensive aspects of software development. Extensive automated testing and code inspections may mitigate their effect, but some code fragments are necessarily more likely to be faulty than others, and automated identification of fault prone modules helps to focus testing and inspections, thus limiting wasted effort and potentially improving detection rates. However, software metrics data is often extremely noisy, with enormous imbalances in the size of the positive and negative classes. In this work, we present a new approach to predictive modelling of fault proneness in software modules, introducing a new feature representation to overcome some of these issues. This rank sum representation offers improved or at worst comparable performance to earlier approaches for standard data sets, and readily allows the user to choose an appropriate trade-off between precision and recall to optimise inspection effort to suit different testing environments. The method is evaluated using the NASA Metrics Data Program (MDP) data sets, and performance is compared with existing studies based on the Support Vector Machine (SVM) and Naïve Bayes (NB) Classifiers, and with our own comprehensive evaluation of these methods.

Prediction of CO concentrations based on a hybrid Partial Least Square and Support Vector Machine model

Due to the health impacts caused by exposures to air pollutants in urban areas, monitoring and forecasting of air quality parameters have become popular as an important topic in atmospheric and environmental research today. The knowledge on the dynamics and complexity of air pollutants behavior has made artificial intelligence models as a useful tool for a more accurate pollutant concentration prediction. This paper focuses on an innovative method of daily air pollution prediction using combination of Support Vector Machine (SVM) as predictor and Partial Least Square (PLS) as a data selection tool based on the measured values of CO concentrations. The CO concentrations of Rey monitoring station in the south of Tehran, from Jan. 2007 to Feb. 2011, have been used to test the effectiveness of this method. The hourly CO concentrations have been predicted using the SVM and the hybrid PLS–SVM models. Similarly, daily CO concentrations have been predicted based on the aforementioned four years measured data. Results demonstrated that both models have good prediction ability; however the hybrid PLS–SVM has better accuracy. In the analysis presented in this paper, statistic estimators including relative mean errors, root mean squared errors and the mean absolute relative error have been employed to compare performances of the models. It has been concluded that the errors decrease after size reduction and coefficients of determination increase from 56 to 81% for SVM model to 65–85% for hybrid PLS–SVM model respectively. Also it was found that the hybrid PLS–SVM model required lower computational time than SVM model as expected, hence supporting the more accurate and faster prediction ability of hybrid PLS–SVM model.

Capturing the ‘authentic voice’ : challenges and opportunities for voice and self-representation in two ABC storytelling projects

This paper discusses the opportunities and challenges that arise within storytelling projects that are facilitated by public service broadcasters and that aim to amplify the voices of ‘ordinary people’. In particular, it focuses on two of the Australian Broadcasting Corporation’s current life storytelling projects: ABC Open and Heywire.

Reflexive Dwelling: The body as representation of wall

In a play-within-a-play, the Mechanicals' production within William Shakespeare's A Midsummer Night's Dream, the character Snout announces his transformation to play the character of Wall. Snout's portrayal of Wall is both comical and menacing as he represents the forces that separate the lovers Pyramus and Thisbe. Wall becomes a subject in a manner no different from the lovers that he separates; his influence on their situation is brought to life. The unbecoming nature of walls to demarcate, separate, intimidate, influence and control is a relationship most can relate to in their experiences with architecture. It is in these moments that architecture leaps from the sphere of object into the realm of subject; where we might be involved in some intense struggle with the placement of a wall, the wall that might separate us from a lover, justice, freedom, power or privacy. This study investigates how this struggle is portrayed through the human body as representation of walls in performance.

Combining word semantics within complex Hilbert space for information retrieval

Complex numbers are a fundamental aspect of the mathematical formalism of quantum physics. Quantum-like models developed outside physics often overlooked the role of complex numbers. Specifically, previous models in Information Retrieval (IR) ignored complex numbers. We argue that to advance the use of quantum models of IR, one has to lift the constraint of real-valued representations of the information space, and package more information within the representation by means of complex numbers. As a first attempt, we propose a complex-valued representation for IR, which explicitly uses complex valued Hilbert spaces, and thus where terms, documents and queries are represented as complex-valued vectors. The proposal consists of integrating distributional semantics evidence within the real component of a term vector; whereas, ontological information is encoded in the imaginary component. Our proposal has the merit of lifting the role of complex numbers from a computational byproduct of the model to the very mathematical texture that unifies different levels of semantic information. An empirical instantiation of our proposal is tested in the TREC Medical Record task of retrieving cohorts for clinical studies.

Secure computation of the vector dominance problem

Suppose two parties, holding vectors A = (a 1,a 2,...,a n ) and B = (b 1,b 2,...,b n ) respectively, wish to know whether a i  > b i for all i, without disclosing any private input. This problem is called the vector dominance problem, and is closely related to the well-studied problem for securely comparing two numbers (Yao’s millionaires problem). In this paper, we propose several protocols for this problem, which improve upon existing protocols on round complexity or communication/computation complexity.

Fault diagnosis of low speed bearing based on acoustic emission signal and multi-class relevance vector machine

This study presents an acoustic emission (AE) based fault diagnosis for low speed bearing using multi-class relevance vector machine (RVM). A low speed test rig was developed to simulate the various defects with shaft speeds as low as 10 rpm under several loading conditions. The data was acquired using anAEsensor with the test bearing operating at a constant loading (5 kN) andwith a speed range from20 to 80 rpm. This study is aimed at finding a reliable method/tool for low speed machines fault diagnosis based on AE signal. In the present study, component analysis was performed to extract the bearing feature and to reduce the dimensionality of original data feature. The result shows that multi-class RVM offers a promising approach for fault diagnosis of low speed machines.