Emotion recognition of static and dynamic faces in autism spectrum disorder

There is substantial evidence for facial emotion recognition (FER) deficits in autism spectrum disorder (ASD). The extent of this impairment, however, remains unclear, and there is some suggestion that clinical groups might benefit from the use of dynamic rather than static images. High-functioning individuals with ASD (n = 36) and typically developing controls (n = 36) completed a computerised FER task involving static and dynamic expressions of the six basic emotions. The ASD group showed poorer overall performance in identifying anger and disgust and were disadvantaged by dynamic (relative to static) stimuli when presented with sad expressions. Among both groups, however, dynamic stimuli appeared to improve recognition of anger. This research provides further evidence of specific impairment in the recognition of negative emotions in ASD, but argues against any broad advantages associated with the use of dynamic displays.

Cross-cultural adaptation, reliability and validity of the Spanish version of the upper limb functional index

Background The Upper Limb Functional Index (ULFI) is an internationally widely used outcome measure with robust, valid psychometric properties. The purpose of study is to develop and validate a ULFI Spanish-version (ULFI-Sp). Methods A two stage observational study was conducted. The ULFI was cross-culturally adapted to Spanish through double forward and backward translations, the psychometric properties were then validated. Participants (n = 126) with various upper limb conditions of >12 weeks duration completed the ULFI-Sp, QuickDASH and the Euroqol Health Questionnaire 5 Dimensions (EQ-5D-3 L). The full sample determined internal consistency, concurrent criterion validity, construct validity and factor structure; a subgroup (n = 35) determined reliability at seven days. Results The ULFI-Sp demonstrated high internal consistency (α = 0.94) and reliability (r = 0.93). Factor structure was one-dimensional and supported construct validity. Criterion validity with the EQ-5D-3 L was fair and inversely correlated (r = −0.59). The QuickDASH data was unavailable for analysis due to excessive missing responses. Conclusions The ULFI-Sp is a valid upper limb outcome measure with similar psychometric properties to the English language version.

Human identification from video using advanced gait recognition techniques

The solutions proposed in this thesis contribute to improve gait recognition performance in practical scenarios that further enable the adoption of gait recognition into real world security and forensic applications that require identifying humans at a distance. Pioneering work has been conducted on frontal gait recognition using depth images to allow gait to be integrated with biometric walkthrough portals. The effects of gait challenging conditions including clothing, carrying goods, and viewpoint have been explored. Enhanced approaches are proposed on segmentation, feature extraction, feature optimisation and classification elements, and state-of-the-art recognition performance has been achieved. A frontal depth gait database has been developed and made available to the research community for further investigation. Solutions are explored in 2D and 3D domains using multiple images sources, and both domain-specific and independent modality gait features are proposed.

Towards pose-robust face recognition on video

This thesis investigates face recognition in video under the presence of large pose variations. It proposes a solution that performs simultaneous detection of facial landmarks and head poses across large pose variations, employs discriminative modelling of feature distributions of faces with varying poses, and applies fusion of multiple classifiers to pose-mismatch recognition. Experiments on several benchmark datasets have demonstrated that improved performance is achieved using the proposed solution.

Developing a Cross-cultural Law Curriculum

This work makes the case that cross cultural issues are central to the purposes of legal education, and no longer can such issues be seen as an add-on to the traditional curriculum. The authors argue instead for a critical multiculturalism that is attuned to questions of gender, class, sexuality and social justice, and that must inform the whole law school curriculum.

Beyond equivalency: Repositioning artistic research within the Australian university sector

Presented in concept at the ACUADS 2008 conference, this paper reports on a research study conducted for PhD into how artistic researchers have been accommodated in the Australian university research management system, and the impacts experienced by artistic researchers through this location. It draws upon a wide range of data to provide the first analysis of this topic reported across all artistic disciplines in Australia in relation to university experiences and updates the Strand Report in 1998 in relation to government policy. Data sources include a correlation of literature from arts disciplines with that of higher education management and government policies; survey responses from of heads of academic units containing artistic researchers in over 40% of Australian universities; interviews with 27 artistic researchers in three case study universities; and interviews with longstanding expert commentators on artistic research and Deputy Vice Chancellors responsible for research. The study suggests that while limited progress has been made towards the acceptance of artistic research as an equivalent and legitimate research endeavour, significant structural, cultural and practical challenges remain which are undermining relationships between universities and their artistic staff and engendering behavioural changes within artistic practitioners that can affect the nature and quality of artistic work that is produced. Reflecting the voices of artistic researchers across the broad visual and performing arts disciplinary spectrum from early to senior career academics, it explores ways forward for universities, and artistic researchers themselves, to secure greater equity and recognition for artistic research.

Text recognition approaches for indoor robotics : a comparison

This paper evaluates the performance of different text recognition techniques for a mobile robot in an indoor (university campus) environment. We compared four different methods: our own approach using existing text detection methods (Minimally Stable Extremal Regions detector and Stroke Width Transform) combined with a convolutional neural network, two modes of the open source program Tesseract, and the experimental mobile app Google Goggles. The results show that a convolutional neural network combined with the Stroke Width Transform gives the best performance in correctly matched text on images with single characters whereas Google Goggles gives the best performance on images with multiple words. The dataset used for this work is released as well.

Machine learning for activity recognition : hip versus wrist data

Problem addressed Wrist-worn accelerometers are associated with greater compliance. However, validated algorithms for predicting activity type from wrist-worn accelerometer data are lacking. This study compared the activity recognition rates of an activity classifier trained on acceleration signal collected on the wrist and hip. Methodology 52 children and adolescents (mean age 13.7 +/- 3.1 year) completed 12 activity trials that were categorized into 7 activity classes: lying down, sitting, standing, walking, running, basketball, and dancing. During each trial, participants wore an ActiGraph GT3X+ tri-axial accelerometer on the right hip and the non-dominant wrist. Features were extracted from 10-s windows and inputted into a regularized logistic regression model using R (Glmnet + L1). Results Classification accuracy for the hip and wrist was 91.0% +/- 3.1% and 88.4% +/- 3.0%, respectively. The hip model exhibited excellent classification accuracy for sitting (91.3%), standing (95.8%), walking (95.8%), and running (96.8%); acceptable classification accuracy for lying down (88.3%) and basketball (81.9%); and modest accuracy for dance (64.1%). The wrist model exhibited excellent classification accuracy for sitting (93.0%), standing (91.7%), and walking (95.8%); acceptable classification accuracy for basketball (86.0%); and modest accuracy for running (78.8%), lying down (74.6%) and dance (69.4%). Potential Impact Both the hip and wrist algorithms achieved acceptable classification accuracy, allowing researchers to use either placement for activity recognition.

Introduction : studying Indonesian media worlds at the intersections of area and cultural studies

When preparing this special issue,1 our discussions with the editorial board of the International Journal of Cultural Studies (IJCS) included a moment of simultaneous surprise and reflection, which we would like to use as a starting point for our introduction to the articles appearing here. This occurred during communications about the number and length of the articles required for a special issue. The board’s representative stipulated that a specific number of articles were to be written by Indonesian scholars. The request surprised us. We had neither discussed nor anticipated ethnic or national quotas for authorial participation. But although the request caught us off guard it also stimulated us to think about the two disciplinary terrains traversed in the articles to follow.

LeaD-In : a cultural change model for peer review of teaching in higher education

Peer review of teaching is recognized increasingly as one strategy for academic development even though historically peer review of teaching is often unsupported by policy, action and culture in many Australian universities. Higher education leaders report that academics generally do not engage with peer review of teaching in a systematic or constructive manner, and this paper advances and analyses a conceptual model to highlight conditions and strategies necessary for the implementation of sustainable peer review in higher education institutions. The model highlights leadership, development and implementation, which are critical to the success and formation of a culture of peer review of teaching. The work arises from collaborative research funded by the Office for Learning and Teaching to foster and advance a culture of peer review of teaching across several universities in Australia.

Towards Vision-Based Pose- and Condition-Invariant Place Recognition along Routes

Vision-based place recognition involves recognising familiar places despite changes in environmental conditions or camera viewpoint (pose). Existing training-free methods exhibit excellent invariance to either of these challenges, but not both simultaneously. In this paper, we present a technique for condition-invariant place recognition across large lateral platform pose variance for vehicles or robots travelling along routes. Our approach combines sideways facing cameras with a new multi-scale image comparison technique that generates synthetic views for input into the condition-invariant Sequence Matching Across Route Traversals (SMART) algorithm. We evaluate the system’s performance on multi-lane roads in two different environments across day-night cycles. In the extreme case of day-night place recognition across the entire width of a four-lane-plus-median-strip highway, we demonstrate performance of up to 44% recall at 100% precision, where current state-of-the-art fails.

Visual place recognition for persistent robot navigation in changing environments

This thesis demonstrates that robots can learn about how the world changes, and can use this information to recognise where they are, even when the appearance of the environment has changed a great deal. The ability to localise in highly dynamic environments using vision only is a key tool for achieving long-term, autonomous navigation in unstructured outdoor environments. The proposed learning algorithms are designed to be unsupervised, and can be generated by the robot online in response to its observations of the world, without requiring information from a human operator or other external source.

Unsupervised online learning of condition-invariant images for place recognition

This paper presents an online, unsupervised training algorithm enabling vision-based place recognition across a wide range of changing environmental conditions such as those caused by weather, seasons, and day-night cycles. The technique applies principal component analysis to distinguish between aspects of a location’s appearance that are condition-dependent and those that are condition-invariant. Removing the dimensions associated with environmental conditions produces condition-invariant images that can be used by appearance-based place recognition methods. This approach has a unique benefit – it requires training images from only one type of environmental condition, unlike existing data-driven methods that require training images with labelled frame correspondences from two or more environmental conditions. The method is applied to two benchmark variable condition datasets. Performance is equivalent or superior to the current state of the art despite the lesser training requirements, and is demonstrated to generalise to previously unseen locations.

Convolutional Neural Network-based place recognition

Recently Convolutional Neural Networks (CNNs) have been shown to achieve state-of-the-art performance on various classification tasks. In this paper, we present for the first time a place recognition technique based on CNN models, by combining the powerful features learnt by CNNs with a spatial and sequential filter. Applying the system to a 70 km benchmark place recognition dataset we achieve a 75% increase in recall at 100% precision, significantly outperforming all previous state of the art techniques. We also conduct a comprehensive performance comparison of the utility of features from all 21 layers for place recognition, both for the benchmark dataset and for a second dataset with more significant viewpoint changes.

Cultural and linguistic localization of games to bridge the digital and cultural divide in indigenous populations

Localization of technology is now widely applied to the preservation and revival of the culture of indigenous peoples around the world, most commonly through the translation into indigenous languages, which has been proven to increase the adoption of technology. However, this current form of localization excludes two demographic groups, which are key to the effectiveness of localization efforts in the African context: the younger generation (under the age of thirty) with an Anglo- American cultural view who have no need or interest in their indigenous culture; and the older generation (over the age of fifty) who are very knowledgeable about their indigenous culture, but have little or no knowledge on the use of a computer. This paper presents the design of a computer game engine that can be used to provide an interface for both technology and indigenous culture learning for both generations. Four indigenous Ugandan games are analyzed and identified for their attractiveness to both generations, to both rural and urban populations, and for their propensity to develop IT skills in older generations.