Preserving the past in digital memory

The process of researching children’s literature from the past is a growing challenge as resources age and are increasingly treated as rare items, stored away within libraries and other research centres. In Australia, researchers and librarians have collaborated with the bibliographic database AustLit: The Australian Literature Resource to produce the Australian Children’s Literature Digital Resources Project (CLDR). This Project aims to address the growing demand for online access to rare children’s literature resources, and demonstrates the research potential of early Australian children’s literature by supplementing the collection with relevant critical articles. The CLDR project is designed with a specific focus and provides access to full text Australian children’s literature from European settlement to 1945. The collection demonstrates a need and desire to preserve literature treasures to prevent losing such collections in a digital age. The collection covers many themes relevant to the conference including, trauma, survival, memory, survival, hauntings, and histories. The resource provides new and exciting ways with which to research children’s literature from the past and offers a fascinating repository to scholars and professionals of ranging disciplines who are in interested in Australian children’s literature.

Spatio temporal feature evaluation for action recognition

Spatio-Temporal interest points are the most popular feature representation in the field of action recognition. A variety of methods have been proposed to detect and describe local patches in video with several techniques reporting state of the art performance for action recognition. However, the reported results are obtained under different experimental settings with different datasets, making it difficult to compare the various approaches. As a result of this, we seek to comprehensively evaluate state of the art spatio- temporal features under a common evaluation framework with popular benchmark datasets (KTH, Weizmann) and more challenging datasets such as Hollywood2. The purpose of this work is to provide guidance for researchers, when selecting features for different applications with different environmental conditions. In this work we evaluate four popular descriptors (HOG, HOF, HOG/HOF, HOG3D) using a popular bag of visual features representation, and Support Vector Machines (SVM)for classification. Moreover, we provide an in-depth analysis of local feature descriptors and optimize the codebook sizes for different datasets with different descriptors. In this paper, we demonstrate that motion based features offer better performance than those that rely solely on spatial information, while features that combine both types of data are more consistent across a variety of conditions, but typically require a larger codebook for optimal performance.

The role of the internet and digital technologies in the struggle for recognition of the forgotten Australians

Our contemporary public sphere has seen the 'emergence of new political rituals, which are concerned with the stains of the past, with self disclosure, and with ways of remembering once taboo and traumatic events' (Misztal, 2005). A recent case of this phenomenon occurred in Australia in 2009 with the apology to the 'Forgotten Australians': a group who suffered abuse and neglect after being removed from their parents – either in Australia or in the UK - and placed in Church and State run institutions in Australia between 1930 and 1970. This campaign for recognition by a profoundly marginalized group coincides with the decade in which the opportunities of Web 2.0 were seen to be diffusing throughout different social groups, and were considered a tool for social inclusion. This paper examines the case of the Forgotten Australians as an opportunity to investigate the role of the internet in cultural trauma and public apology. As such, it adds to recent scholarship on the role of digital web based technologies in commemoration and memorials (Arthur, 2009; Haskins, 2007; Cohen and Willis, 2004), and on digital storytelling in the context of trauma (Klaebe, 2011) by locating their role in a broader and emerging domain of social responsibility and political action (Alexander, 2004).

Kernel analysis on Grassmann manifolds for action recognition

Modelling video sequences by subspaces has recently shown promise for recognising human actions. Subspaces are able to accommodate the effects of various image variations and can capture the dynamic properties of actions. Subspaces form a non-Euclidean and curved Riemannian manifold known as a Grassmann manifold. Inference on manifold spaces usually is achieved by embedding the manifolds in higher dimensional Euclidean spaces. In this paper, we instead propose to embed the Grassmann manifolds into reproducing kernel Hilbert spaces and then tackle the problem of discriminant analysis on such manifolds. To achieve efficient machinery, we propose graph-based local discriminant analysis that utilises within-class and between-class similarity graphs to characterise intra-class compactness and inter-class separability, respectively. Experiments on KTH, UCF Sports, and Ballet datasets show that the proposed approach obtains marked improvements in discrimination accuracy in comparison to several state-of-the-art methods, such as the kernel version of affine hull image-set distance, tensor canonical correlation analysis, spatial-temporal words and hierarchy of discriminative space-time neighbourhood features.

Image Region Salience for Improving Appearance-Based Place Recognition using a Supervised Classifier System

Many state of the art vision-based Simultaneous Localisation And Mapping (SLAM) and place recognition systems compute the salience of visual features in their environment. As computing salience can be problematic in radically changing environments new low resolution feature-less systems have been introduced, such as SeqSLAM, all of which consider the whole image. In this paper, we implement a supervised classifier system (UCS) to learn the salience of image regions for place recognition by feature-less systems. SeqSLAM only slightly benefits from the results of training, on the challenging real world Eynsham dataset, as it already appears to filter less useful regions of a panoramic image. However, when recognition is limited to specific image regions performance improves by more than an order of magnitude by utilising the learnt image region saliency. We then investigate whether the region salience generated from the Eynsham dataset generalizes to another car-based dataset using a perspective camera. The results suggest the general applicability of an image region salience mask for optimizing route-based navigation applications.

On robust face recognition via sparse encoding : the good, the bad, and the ugly

In the field of face recognition, Sparse Representation (SR) has received considerable attention during the past few years. Most of the relevant literature focuses on holistic descriptors in closed-set identification applications. The underlying assumption in SR-based methods is that each class in the gallery has sufficient samples and the query lies on the subspace spanned by the gallery of the same class. Unfortunately, such assumption is easily violated in the more challenging face verification scenario, where an algorithm is required to determine if two faces (where one or both have not been seen before) belong to the same person. In this paper, we first discuss why previous attempts with SR might not be applicable to verification problems. We then propose an alternative approach to face verification via SR. Specifically, we propose to use explicit SR encoding on local image patches rather than the entire face. The obtained sparse signals are pooled via averaging to form multiple region descriptors, which are then concatenated to form an overall face descriptor. Due to the deliberate loss spatial relations within each region (caused by averaging), the resulting descriptor is robust to misalignment & various image deformations. Within the proposed framework, we evaluate several SR encoding techniques: l1-minimisation, Sparse Autoencoder Neural Network (SANN), and an implicit probabilistic technique based on Gaussian Mixture Models. Thorough experiments on AR, FERET, exYaleB, BANCA and ChokePoint datasets show that the proposed local SR approach obtains considerably better and more robust performance than several previous state-of-the-art holistic SR methods, in both verification and closed-set identification problems. The experiments also show that l1-minimisation based encoding has a considerably higher computational than the other techniques, but leads to higher recognition rates.

Cohort normalization based sparse representation for undersampled face recognition

Abstract. In recent years, sparse representation based classification(SRC) has received much attention in face recognition with multipletraining samples of each subject. However, it cannot be easily applied toa recognition task with insufficient training samples under uncontrolledenvironments. On the other hand, cohort normalization, as a way of mea-suring the degradation effect under challenging environments in relationto a pool of cohort samples, has been widely used in the area of biometricauthentication. In this paper, for the first time, we introduce cohort nor-malization to SRC-based face recognition with insufficient training sam-ples. Specifically, a user-specific cohort set is selected to normalize theraw residual, which is obtained from comparing the test sample with itssparse representations corresponding to the gallery subject, using poly-nomial regression. Experimental results on AR and FERET databases show that cohort normalization can bring SRC much robustness against various forms of degradation factors for undersampled face recognition.

Heteroscedastic probabilistic linear discriminant analysis for manifold learning in video-based face recognition

To recognize faces in video, face appearances have been widely modeled as piece-wise local linear models which linearly approximate the smooth yet non-linear low dimensional face appearance manifolds. The choice of representations of the local models is crucial. Most of the existing methods learn each local model individually meaning that they only anticipate variations within each class. In this work, we propose to represent local models as Gaussian distributions which are learned simultaneously using the heteroscedastic probabilistic linear discriminant analysis (PLDA). Each gallery video is therefore represented as a collection of such distributions. With the PLDA, not only the within-class variations are estimated during the training, the separability between classes is also maximized leading to an improved discrimination. The heteroscedastic PLDA itself is adapted from the standard PLDA to approximate face appearance manifolds more accurately. Instead of assuming a single global within-class covariance, the heteroscedastic PLDA learns different within-class covariances specific to each local model. In the recognition phase, a probe video is matched against gallery samples through the fusion of point-to-model distances. Experiments on the Honda and MoBo datasets have shown the merit of the proposed method which achieves better performance than the state-of-the-art technique.

Improving opportunity recognition and business performance in small and medium manufacturing enterprises through design innovation programs

Increasing awareness of the benefits of stimulating entrepreneurial behaviour in small and medium enterprises has fostered strong interest in innovation programs. Recently many western countries have invested in design innovation for better firm performance. This research presents some early findings from a study of companies that participated in a holistic approach to design innovation, where the outcomes include better business performance and better market positioning in global markets. Preliminary findings from in-depth semi-structured interviews indicate the importance of firm openness to new ways of working and to developing new processes of strategic entrepreneurship. Implications for theory and practice are discussed.

How the environment affects opportunity recognition by entrepreneurs at the bottom of the pyramid

There is an army of bottom of the pyramid entrepreneurs (BOPE) who have the potential to transform developing economies, if they can identify and exploit business opportunities. BOPE could have unidentified resources that could lead to the recognition of radical new opportunities. This study paper asks how environmental factors and identification of resources affect Opportunity Recognition by BOP entrepreneurs in developing economies. To investigate this research question we conduct a literature review and plan semi-structured interviews of existing and nascent entrepreneurs in the largest and arguably the poorest country in Africa, the Democratic Republic of the Congo. In this paper we review the context of BOPE and describe the methodology we will use to gather and analyse data. Finally, we describe our access to suitable respondents for this study and how it will be conducted.

I-vector based speaker recognition using advanced channel compensation techniques

This paper investigates advanced channel compensation techniques for the purpose of improving i-vector speaker verification performance in the presence of high intersession variability using the NIST 2008 and 2010 SRE corpora. The performance of four channel compensation techniques: (a) weighted maximum margin criterion (WMMC), (b) source-normalized WMMC (SN-WMMC), (c) weighted linear discriminant analysis (WLDA), and; (d) source-normalized WLDA (SN-WLDA) have been investigated. We show that, by extracting the discriminatory information between pairs of speakers as well as capturing the source variation information in the development i-vector space, the SN-WLDA based cosine similarity scoring (CSS) i-vector system is shown to provide over 20% improvement in EER for NIST 2008 interview and microphone verification and over 10% improvement in EER for NIST 2008 telephone verification, when compared to SN-LDA based CSS i-vector system. Further, score-level fusion techniques are analyzed to combine the best channel compensation approaches, to provide over 8% improvement in DCF over the best single approach, (SN-WLDA), for NIST 2008 interview/ telephone enrolment-verification condition. Finally, we demonstrate that the improvements found in the context of CSS also generalize to state-of-the-art GPLDA with up to 14% relative improvement in EER for NIST SRE 2010 interview and microphone verification and over 7% relative improvement in EER for NIST SRE 2010 telephone verification.

Does Identifying Ambushers as Non-Sponsors Help or Hurt Legitimate Sponsors? Memory and Attitudinal Consequences

Two experiments examine outcomes for sponsor and ambusher brands within sponsorship settings. It is demonstrated that although making consumers aware of the presence of ambusher brands can reduce subsequent event recall to competitor cues, recall to sponsor cues can also suffer. Attitudinal effects are also considered.

Managing memory and reducing I/O cost for correlation matrix calculation in bioinformatics

The generation of a correlation matrix from a large set of long gene sequences is a common requirement in many bioinformatics problems such as phylogenetic analysis. The generation is not only computationally intensive but also requires significant memory resources as, typically, few gene sequences can be simultaneously stored in primary memory. The standard practice in such computation is to use frequent input/output (I/O) operations. Therefore, minimizing the number of these operations will yield much faster run-times. This paper develops an approach for the faster and scalable computing of large-size correlation matrices through the full use of available memory and a reduced number of I/O operations. The approach is scalable in the sense that the same algorithms can be executed on different computing platforms with different amounts of memory and can be applied to different problems with different correlation matrix sizes. The significant performance improvement of the approach over the existing approaches is demonstrated through benchmark examples.

Can testosterone replacement decrease the memory problem of old age?

The world is rapidly ageing. It is against this backdrop that there are increasing incidences of dementia reported worldwide, with Alzheimer's disease (AD) being the most common form of dementia in the elderly. It is estimated that AD affects almost 4 million people in the US, and costs the US economy more than 65 million dollars annually. There is currently no cure for AD but various therapeutic agents have been employed in attempting to slow down the progression of the illness, one of which is oestrogen. Over the last decades, scientists have focused mainly on the roles of oestrogen in the prevention and treatment of AD. Newer evidences suggested that testosterone might also be involved in the pathogenesis of AD. Although the exact mechanisms on how androgen might affect AD are still largely unknown, it is known that testosterone can act directly via androgen receptor-dependent mechanisms or indirectly by converting to oestrogen to exert this effect. Clinical trials need to be conducted to ascertain the putative role of androgen replacement in Alzheimer's disease.

Insect odour perception: recognition of odour components by flower foraging moths

Odours emitted by flowers are complex blends of volatile compounds. These odours are learnt by flower-visiting insect species, improving their recognition of rewarding flowers and thus foraging efficiency. We investigated the flexibility of floral odour learning by testing whether adult moths recognize single compounds common to flowers on which they forage. Dual choice preference tests on Helicoverpa armigera moths allowed free flying moths to forage on one of three flower species; Argyranthemum frutescens (federation daisy), Cajanus cajan (pigeonpea) or Nicotiana tabacum (tobacco). Results showed that, (i) a benzenoid (phenylacetaldehyde) and a monoterpene (linalool) were subsequently recognized after visits to flowers that emitted these volatile constituents, (ii) in a preference test, other monoterpenes in the flowers' odour did not affect the moths' ability to recognize the monoterpene linalool and (iii) relative preferences for two volatiles changed after foraging experience on a single flower species that emitted both volatiles. The importance of using free flying insects and real flowers to understand the mechanisms involved in floral odour learning in nature are discussed in the context of our findings.