Teachers' recognition and referral of anxiety disorders in primary school children

This study investigated the ability of primary school teachers to recognise and refer children with anxiety symptoms. Two hundred and ninety-nine primary school teachers completed a questionnaire exploring their recognition and referral responses to five hypothetical vignettes that described boys and girls with varying severity of anxiety symptoms. Results revealed that teachers were generally able to recognise and make the decision to refer children with severe levels of anxiety. However, they had difficulty distinguishing between children with moderate anxiety symptoms and a severe anxiety disorder. Female teachers were more likely to refer children than were male teachers. The implications and future research are discussed.

Discovering the best feature extraction and selection algorithms for spontaneous facial expression recognition

Feature extraction and selection are critical processes in developing facial expression recognition (FER) systems. While many algorithms have been proposed for these processes, direct comparison between texture, geometry and their fusion, as well as between multiple selection algorithms has not been found for spontaneous FER. This paper addresses this issue by proposing a unified framework for a comparative study on the widely used texture (LBP, Gabor and SIFT) and geometric (FAP) features, using Adaboost, mRMR and SVM feature selection algorithms. Our experiments on the Feedtum and NVIE databases demonstrate the benefits of fusing geometric and texture features, where SIFT+FAP shows the best performance, while mRMR outperforms Adaboost and SVM. In terms of computational time, LBP and Gabor perform better than SIFT. The optimal combination of SIFT+FAP+mRMR also exhibits a state-of-the-art performance.

A subject-specific model of human buttocks and thighs in a seated posture

Finite element analyses of the human body in seated postures requires digital models capable of providing accurate and precise prediction of the tissue-level response of the body in the seated posture. To achieve such models, the human anatomy must be represented with high fidelity. This information can readily be defined using medical imaging techniques such as Magnetic Resonance Imaging (MRI) or Computed Tomography (CT). Current practices for constructing digital human models, based on the magnetic resonance (MR) images, in a lying down (supine) posture have reduced the error in the geometric representation of human anatomy relative to reconstructions based on data from cadaveric studies. Nonetheless, the significant differences between seated and supine postures in segment orientation, soft-tissue deformation and soft tissue strain create a need for data obtained in postures more similar to the application posture. In this study, we present a novel method for creating digital human models based on seated MR data. An adult-male volunteer was scanned in a simulated driving posture using a FONAR 0.6T upright MRI scanner with a T1 scanning protocol. To compensate for unavoidable image distortion near the edges of the study, images of the same anatomical structures were obtained in transverse and sagittal planes. Combinations of transverse and sagittal images were used to reconstruct the major anatomical features from the buttocks through the knees, including bone, muscle and fat tissue perimeters, using Solidworks® software. For each MR image, B-splines were created as contours for the anatomical structures of interest, and LOFT commands were used to interpolate between the generated Bsplines. The reconstruction of the pelvis, from MR data, was enhanced by the use of a template model generated in previous work CT images. A non-rigid registration algorithm was used to fit the pelvis template into the MR data. Additionally, MR image processing was conducted to both the left and the right sides of the model due to the intended asymmetric posture of the volunteer during the MR measurements. The presented subject-specific, three-dimensional model of the buttocks and thighs will add value to optimisation cycles in automotive seat development when used in simulating human interaction with automotive seats.

Mining a data reasoning model for personalized text classification

It is a big challenge to acquire correct user profiles for personalized text classification since users may be unsure in providing their interests. Traditional approaches to user profiling adopt machine learning (ML) to automatically discover classification knowledge from explicit user feedback in describing personal interests. However, the accuracy of ML-based methods cannot be significantly improved in many cases due to the term independence assumption and uncertainties associated with them. This paper presents a novel relevance feedback approach for personalized text classification. It basically applies data mining to discover knowledge from relevant and non-relevant text and constraints specific knowledge by reasoning rules to eliminate some conflicting information. We also developed a Dempster-Shafer (DS) approach as the means to utilise the specific knowledge to build high-quality data models for classification. The experimental results conducted on Reuters Corpus Volume 1 and TREC topics support that the proposed technique achieves encouraging performance in comparing with the state-of-the-art relevance feedback models.

Development and piloting of a brain tumour-specific question prompt list

The objective of this research was to develop a question prompt list aimed at increasing question asking and reducing the unmet information needs of adults with primary brain tumours, and to pilot the question prompt list to determine its suitability for the intended population. Thematic analysis of existing resources was used to create a draft which was refined via interviews with 12 brain tumour patients and six relatives, readability testing and review by health professionals. A non-randomised before–after pilot study with 20 brain tumour patients was used to assess the acceptability and usefulness of the question prompt list, compared with a ‘standard brochure’, and the feasibility of evaluation strategies. The question prompt list developed covered seven main topics (diagnosis, prognosis, symptoms and changes, treatment, support, after treatment finishes and the health professional team). Pilot study participants provided with the question prompt list agreed that it was helpful (7/7), contained questions that were useful to them (7/7) and prompted them to ask their medical oncologist questions (5/7). The question prompt list is acceptable to patients and contains questions relevant to them. Research is now needed to assess its effectiveness in increasing question asking and reducing unmet information needs.

Feature-domain super-resolution framework for gabor-based face and iris recognition

The low resolution of images has been one of the major limitations in recognising humans from a distance using their biometric traits, such as face and iris. Superresolution has been employed to improve the resolution and the recognition performance simultaneously, however the majority of techniques employed operate in the pixel domain, such that the biometric feature vectors are extracted from a super-resolved input image. Feature-domain superresolution has been proposed for face and iris, and is shown to further improve recognition performance by capitalising on direct super-resolving the features which are used for recognition. However, current feature-domain superresolution approaches are limited to simple linear features such as Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA), which are not the most discriminant features for biometrics. Gabor-based features have been shown to be one of the most discriminant features for biometrics including face and iris. This paper proposes a framework to conduct super-resolution in the non-linear Gabor feature domain to further improve the recognition performance of biometric systems. Experiments have confirmed the validity of the proposed approach, demonstrating superior performance to existing linear approaches for both face and iris biometrics.

Probabilistic matching of image sets for video-based face recognition

We address the problem of face recognition on video by employing the recently proposed probabilistic linear discrimi-nant analysis (PLDA). The PLDA has been shown to be robust against pose and expression in image-based face recognition. In this research, the method is extended and applied to video where image set to image set matching is performed. We investigate two approaches of computing similarities between image sets using the PLDA: the closest pair approach and the holistic sets approach. To better model face appearances in video, we also propose the heteroscedastic version of the PLDA which learns the within-class covariance of each individual separately. Our experi-ments on the VidTIMIT and Honda datasets show that the combination of the heteroscedastic PLDA and the closest pair approach achieves the best performance.

On the application of the probabilistic linear discriminant analysis to face recognition across expression

Facial expression is one of the main issues of face recognition in uncontrolled environments. In this paper, we apply the probabilistic linear discriminant analysis (PLDA) method to recognize faces across expressions. Several PLDA approaches are tested and cross-evaluated on the Cohn-Kanade and JAFFE databases. With less samples per gallery subject, high recognition rates comparable to previous works have been achieved indicating the robustness of the approaches. Among the approaches, the mixture of PLDAs has demonstrated better performances. The experimental results also indicate that facial regions around the cheeks, eyes, and eyebrows are more discriminative than regions around the mouth, jaw, chin, and nose.

African Burkitt Lymphoma: Age-Specific Risk and Correlations with Malaria Biomarkers

African Burkitt lymphoma is an aggressive B-cell, non-Hodgkin lymphoma linked to Plasmodium falciparum malaria. Malaria biomarkers related to onset of African Burkitt lymphoma are unknown. We correlated age-specific patterns of 2,602 cases of African Burkitt lymphoma (60% male, mean ± SD age = 7.1 ± 2.9 years) from Uganda, Ghana, and Tanzania with malaria biomarkers published from these countries. Age-specific patterns of this disease and mean multiplicity of P. falciparum malaria parasites, defined as the average number of distinct genotypes per positive blood sample based on the merozoite surface protein-2 assessed by polymerase chain reaction, were correlated and both peaked between 5 and 9 years. This pattern, which was strong and consistent across regions, contrasted parasite prevalence, which peaked at 2 years and decreased slightly, and geometric mean parasite density, which peaked between 2 and 3 years and decreased sharply. Our findings suggest that concurrent infection with multiple malaria genotypes may be related to onset of African Burkitt lymphoma.

Improved facial expression recognition via uni-hyperplane classification

Large margin learning approaches, such as support vector machines (SVM), have been successfully applied to numerous classification tasks, especially for automatic facial expression recognition. The risk of such approaches however, is their sensitivity to large margin losses due to the influence from noisy training examples and outliers which is a common problem in the area of affective computing (i.e., manual coding at the frame level is tedious so coarse labels are normally assigned). In this paper, we leverage the relaxation of the parallel-hyperplanes constraint and propose the use of modified correlation filters (MCF). The MCF is similar in spirit to SVMs and correlation filters, but with the key difference of optimizing only a single hyperplane. We demonstrate the superiority of MCF over current techniques on a battery of experiments.

Evaluation of image resolution and super-resolution on face recognition performance

While researchers strive to improve automatic face recognition performance, the relationship between image resolution and face recognition performance has not received much attention. This relationship is examined systematically and a framework is developed such that results from super-resolution techniques can be compared. Three super-resolution techniques are compared with the Eigenface and Elastic Bunch Graph Matching face recognition engines. Parameter ranges over which these techniques provide better recognition performance than interpolated images is determined.

Perceiving and pursuing novelty : a grounded theory of adolescent creativity

Creativity plays an increasingly important role in our personal, social, educational, and community lives. For adolescents, creativity can enable self-expression, be a means of pushing boundaries, and assist learning, achievement, and completion of everyday tasks. Moreover, adolescents who demonstrate creativity can potentially enhance their capacity to face unknown future challenges, address mounting social and ecological issues in our global society, and improve their career opportunities and contribution to the economy. For these reasons, creativity is an essential capacity for young people in their present and future, and is highlighted as a priority in current educational policy nationally and internationally. Despite growing recognition of creativity’s importance and attention to creativity in research, the creative experience from the perspectives of the creators themselves and the creativity of adolescents are neglected fields of study. Hence, this research investigated adolescents’ self-reported experiences of creativity to improve understandings of their creative processes and manifestations, and how these can be supported or inhibited. Although some aspects of creativity have been extensively researched, there were no comprehensive, multidisciplinary theoretical frameworks of adolescent creativity to provide a foundation for this study. Therefore, a grounded theory methodology was adopted for the purpose of constructing a new theory to describe and explain adolescents’ creativity in a range of domains. The study’s constructivist-interpretivist perspective viewed the data and findings as interpretations of adolescents’ creative experiences, co-constructed by the participants and the researcher. The research was conducted in two academically selective high schools in Australia: one arts school, and one science, mathematics, and technology school. Twenty adolescent participants (10 from each school) were selected using theoretical sampling. Data were collected via focus groups, individual interviews, an online discussion forum, and email communications. Grounded theory methods informed a process of concurrent data collection and analysis; each iteration of analysis informed subsequent data collection. Findings portray creativity as it was perceived and experienced by participants, presented in a Grounded Theory of Adolescent Creativity. The Grounded Theory of Adolescent Creativity comprises a core category, Perceiving and Pursuing Novelty: Not the Norm, which linked all findings in the study. This core category explains how creativity involved adolescents perceiving stimuli and experiences differently, approaching tasks or life unconventionally, and pursuing novel ideas to create outcomes that are not the norm when compared with outcomes by peers. Elaboration of the core category is provided by the major categories of findings. That is, adolescent creativity entailed utilising a network of Sub-Processes of Creativity, using strategies for Managing Constraints and Challenges, and drawing on different Approaches to Creativity – adaptation, transfer, synthesis, and genesis – to apply the sub-processes and produce creative outcomes. Potentially, there were Effects of Creativity on Creators and Audiences, depending on the adolescent and the task. Three Types of Creativity were identified as the manifestations of the creative process: creative personal expression, creative boundary pushing, and creative task achievement. Interactions among adolescents’ dispositions and environments were influential in their creativity. Patterns and variations of these interactions revealed a framework of four Contexts for Creativity that offered different levels of support for creativity: high creative disposition–supportive environment; high creative disposition–inhibiting environment; low creative disposition–supportive environment; and low creative disposition–inhibiting environment. These contexts represent dimensional ranges of how dispositions and environments supported or inhibited creativity, and reveal that the optimal context for creativity differed depending on the adolescent, task, domain, and environment. This study makes four main contributions, which have methodological and theoretical implications for researchers, as well as practical implications for adolescents, parents, teachers, policy and curriculum developers, and other interested stakeholders who aim to foster the creativity of adolescents. First, this study contributes methodologically through its constructivist-interpretivist grounded theory methodology combining the grounded theory approaches of Corbin and Strauss (2008) and Charmaz (2006). Innovative data collection was also demonstrated through integration of data from online and face-to-face interactions with adolescents, within the grounded theory design. These methodological contributions have broad applicability to researchers examining complex constructs and processes, and with populations who integrate multimedia as a natural form of communication. Second, applicable to creativity in diverse domains, the Grounded Theory of Adolescent Creativity supports a hybrid view of creativity as both domain-general and domain-specific. A third major contribution was identification of a new form of creativity, educational creativity (ed-c), which categorises creativity for learning or achievement within the constraints of formal educational contexts. These theoretical contributions inform further research about creativity in different domains or multidisciplinary areas, and with populations engaged in formal education. However, the key contribution of this research is that it presents an original Theory and Model of Adolescent Creativity to explain the complex, multifaceted phenomenon of adolescents’ creative experiences.

PLDA based speaker recognition on short utterances

This paper investigates the effects of limited speech data in the context of speaker verification using a probabilistic linear discriminant analysis (PLDA) approach. Being able to reduce the length of required speech data is important to the development of automatic speaker verification system in real world applications. When sufficient speech is available, previous research has shown that heavy-tailed PLDA (HTPLDA) modeling of speakers in the i-vector space provides state-of-the-art performance, however, the robustness of HTPLDA to the limited speech resources in development, enrolment and verification is an important issue that has not yet been investigated. In this paper, we analyze the speaker verification performance with regards to the duration of utterances used for both speaker evaluation (enrolment and verification) and score normalization and PLDA modeling during development. Two different approaches to total-variability representation are analyzed within the PLDA approach to show improved performance in short-utterance mismatched evaluation conditions and conditions for which insufficient speech resources are available for adequate system development. The results presented within this paper using the NIST 2008 Speaker Recognition Evaluation dataset suggest that the HTPLDA system can continue to achieve better performance than Gaussian PLDA (GPLDA) as evaluation utterance lengths are decreased. We also highlight the importance of matching durations for score normalization and PLDA modeling to the expected evaluation conditions. Finally, we found that a pooled total-variability approach to PLDA modeling can achieve better performance than the traditional concatenated total-variability approach for short utterances in mismatched evaluation conditions and conditions for which insufficient speech resources are available for adequate system development.

Visual route recognition with a handful of bits

In this paper we use a sequence-based visual localization algorithm to reveal surprising answers to the question, how much visual information is actually needed to conduct effective navigation? The algorithm actively searches for the best local image matches within a sliding window of short route segments or 'sub-routes', and matches sub-routes by searching for coherent sequences of local image matches. In contract to many existing techniques, the technique requires no pre-training or camera parameter calibration. We compare the algorithm's performance to the state-of-the-art FAB-MAP 2.0 algorithm on a 70 km benchmark dataset. Performance matches or exceeds the state of the art feature-based localization technique using images as small as 4 pixels, fields of view reduced by a factor of 250, and pixel bit depths reduced to 2 bits. We present further results demonstrating the system localizing in an office environment with near 100% precision using two 7 bit Lego light sensors, as well as using 16 and 32 pixel images from a motorbike race and a mountain rally car stage. By demonstrating how little image information is required to achieve localization along a route, we hope to stimulate future 'low fidelity' approaches to visual navigation that complement probabilistic feature-based techniques.