Postcode segmentation and recognition using projections and bispectral features

A system to segment and recognize Australian 4-digit postcodes from address labels on parcels is described. Images of address labels are preprocessed and adaptively thresholded to reduce noise. Projections are used to segment the line and then the characters comprising the postcode. Individual digits are recognized using bispectral features extracted from their parallel beam projections. These features are insensitive to translation, scaling and rotation, and robust to noise. Results on scanned images are presented. The system is currently being improved and implemented to work on-line.

Super-resolved faces for improved face recognition from surveillance video advances in biometrics

Characteristics of surveillance video generally include low resolution and poor quality due to environmental, storage and processing limitations. It is extremely difficult for computers and human operators to identify individuals from these videos. To overcome this problem, super-resolution can be used in conjunction with an automated face recognition system to enhance the spatial resolution of video frames containing the subject and narrow down the number of manual verifications performed by the human operator by presenting a list of most likely candidates from the database. As the super-resolution reconstruction process is ill-posed, visual artifacts are often generated as a result. These artifacts can be visually distracting to humans and/or affect machine recognition algorithms. While it is intuitive that higher resolution should lead to improved recognition accuracy, the effects of super-resolution and such artifacts on face recognition performance have not been systematically studied. This paper aims to address this gap while illustrating that super-resolution allows more accurate identification of individuals from low-resolution surveillance footage. The proposed optical flow-based super-resolution method is benchmarked against Baker et al.’s hallucination and Schultz et al.’s super-resolution techniques on images from the Terrascope and XM2VTS databases. Ground truth and interpolated images were also tested to provide a baseline for comparison. Results show that a suitable super-resolution system can improve the discriminability of surveillance video and enhance face recognition accuracy. The experiments also show that Schultz et al.’s method fails when dealing surveillance footage due to its assumption of rigid objects in the scene. The hallucination and optical flow-based methods performed comparably, with the optical flow-based method producing less visually distracting artifacts that interfered with human recognition.

High-Order Concept Associations Mining and Inferential Language Modeling for Online Review Spam Detection

Despite many incidents about fake online consumer reviews have been reported, very few studies have been conducted to date to examine the trustworthiness of online consumer reviews. One of the reasons is the lack of an effective computational method to separate the untruthful reviews (i.e., spam) from the legitimate ones (i.e., ham) given the fact that prominent spam features are often missing in online reviews. The main contribution of our research work is the development of a novel review spam detection method which is underpinned by an unsupervised inferential language modeling framework. Another contribution of this work is the development of a high-order concept association mining method which provides the essential term association knowledge to bootstrap the performance for untruthful review detection. Our experimental results confirm that the proposed inferential language model equipped with high-order concept association knowledge is effective in untruthful review detection when compared with other baseline methods.

High-probability regret bounds for bandit online linear optimization

We present a modification of the algorithm of Dani et al. [8] for the online linear optimization problem in the bandit setting, which with high probability has regret at most O ∗ ( √ T) against an adaptive adversary. This improves on the previous algorithm [8] whose regret is bounded in expectation against an oblivious adversary. We obtain the same dependence on the dimension (n 3/2) as that exhibited by Dani et al. The results of this paper rest firmly on those of [8] and the remarkable technique of Auer et al. [2] for obtaining high probability bounds via optimistic estimates. This paper answers an open question: it eliminates the gap between the high-probability bounds obtained in the full-information vs bandit settings.

The differential recognition of children’s cultural practices in middle primary literacy classrooms

This paper argues that teachers’ recognition of children’s cultural practices is an important positive step in helping socio-economically disadvantaged children engage with school literacies. Based on twenty-one longitudinal case studies of children’s literacy development over a three-year period, the authors demonstrate that when children’s knowledges and practices assembled in home and community spheres are treated as valuable material for school learning, children are more likely to invest in the work of acquiring school literacies. However they show also that whilst some children benefit greatly from being allowed to draw on their knowledge of popular culture, sports and the outdoors, other children’s interests may be ignored or excluded. Some differences in teachers’ valuing of home and community cultures appeared to relate to gender dimensions.

Can audio-visual speech recognition outperform acoustically enhanced speech recognition in automotive environment?

The use of visual features in the form of lip movements to improve the performance of acoustic speech recognition has been shown to work well, particularly in noisy acoustic conditions. However, whether this technique can outperform speech recognition incorporating well-known acoustic enhancement techniques, such as spectral subtraction, or multi-channel beamforming is not known. This is an important question to be answered especially in an automotive environment, for the design of an efficient human-vehicle computer interface. We perform a variety of speech recognition experiments on a challenging automotive speech dataset and results show that synchronous HMM-based audio-visual fusion can outperform traditional single as well as multi-channel acoustic speech enhancement techniques. We also show that further improvement in recognition performance can be obtained by fusing speech-enhanced audio with the visual modality, demonstrating the complementary nature of the two robust speech recognition approaches.

Perceptions of online learning in an Australian university : Malaysian students' perspectives

Several researchers have reported that cultural and language differences can affect online interactions and communications between students from different cultural backgrounds. Other researchers have asserted that online learning is a tool that can improve teaching and learning skills, but, its effectiveness depends on how the tool is used. Therefore, this study aims to investigate the kinds of challenges encountered by the international students and how they actually cope with online learning. To date little research exists on the perceptions of online learning environments by international Asian students, in particular Malaysian students who study in Australian Universities; hence this study aims to fill this gap. A mixed-method approach was used to collect quantitative and qualitative data using a modified Online Learning Environment Survey (OLES) instrument and focus group interviews. The sample comprised 76 international students from a university in Brisbane. Thirty-five domestic Australian students were included for comparison. Contrary to assumptions from previous research, the findings revealed that there were few differences between the international Asian students from Malaysia and Australian students with regard to their perceptions of online learning. Another cogent finding that emerged was that online learning was most effective when included within blended learning environments. The students clearly indicated that when learning in a blended environment, it was imperative that appropriate features are blended in and customised to suit the particular needs of international students. The study results indicated that the university could improve the quality of the blended online learning environment by: 1) establishing and maintaining a sense of learning community; 2) enhancing the self motivation of students; and 3) professional development of lecturers/tutors, unit coordinators and learning support personnel. Feedback from focus group interviews, highlighted the students‘ frustration with a lack of cooperative learning, strategies and skills which were expected of them by their lecturers/tutors in order to work productively in groups. They indicated a strong desire for lecturers/tutors to provide them prior training in these strategies and skills. The students identified four ways to optimise learning opportunities in cross-cultural spaces. These were: 1) providing preparatory and ongoing workshops focusing on the dispositions and roles of students within student-centred online learning environments; 2) providing preparatory and ongoing workshops on collaborative group learning strategies and skills; 3) providing workshops familiarising students with Australian culture and language; and 4) providing workshops on strategies for addressing technical problems. Students also indicated a strong desire for professional development of lecturers/tutors focused on: 1) teacher attributes, 2) ways to culturally sensitive curricula, and 3) collaborative learning and cooperative working strategies and skills, and 4) designing flexible program structures. Recommendations from this study will be useful to Australian universities where Asian international students from Malaysia study in blended learning environments. An induction program (online skills, collaborative and teamwork skills, study expectations plus familiarisation with Australian culture) for overseas students at the commencement of their studies; a cultural awareness program for lecturers (cultural sensitivity, ways to communicate and a better understanding of Asian educational systems), upskilling of lecturers‘ ability to structure their teaching online and to apply strong theoretical underpinnings when designing learning activities such as discussion forums, and consistency with regards to how content is located and displayed in a learning management system like Blackboard. Through addressing the research questions in this study, the researcher hopes to contribute to and advance the domain of knowledge related to online learning, and to better understand how international Malaysian students‘ perceive online learning environments. These findings have theoretical and pragmatic significance.

Sparse temporal representations for facial expression recognition

In automatic facial expression recognition, an increasing number of techniques had been proposed for in the literature that exploits the temporal nature of facial expressions. As all facial expressions are known to evolve over time, it is crucially important for a classifier to be capable of modelling their dynamics. We establish that the method of sparse representation (SR) classifiers proves to be a suitable candidate for this purpose, and subsequently propose a framework for expression dynamics to be efficiently incorporated into its current formulation. We additionally show that for the SR method to be applied effectively, then a certain threshold on image dimensionality must be enforced (unlike in facial recognition problems). Thirdly, we determined that recognition rates may be significantly influenced by the size of the projection matrix \Phi. To demonstrate these, a battery of experiments had been conducted on the CK+ dataset for the recognition of the seven prototypic expressions - anger, contempt, disgust, fear, happiness, sadness and surprise - and comparisons have been made between the proposed temporal-SR against the static-SR framework and state-of-the-art support vector machine.

Connecting online with external students : do we really know how to?

Students are often time poor and find it difficult to manage their time in relation to study and other external factors including work. Online study is no exception to this and in many cases where the student is studying in an online only environment, they are also working in full time employment. Higher Education institutions are now offering an abundance of courses online to attract more under-graduate and post-graduate students. It is in this sense that there is an ever-increasing need to understand the student of today and find ways to connect with them and support them in their studies. This paper will report on a small-scale case study of an undergraduate online-only group of first year education students and their associated online experiences in developing a sense of community whilst interacting with a learning management system and its associated tools. Further the paper will explore the mis-conceptions that are widely held by course designers and lecturers involved with online courses.

i-vector based speaker recognition on short utterances

Robust speaker verification on short utterances remains a key consideration when deploying automatic speaker recognition, as many real world applications often have access to only limited duration speech data. This paper explores how the recent technologies focused around total variability modeling behave when training and testing utterance lengths are reduced. Results are presented which provide a comparison of Joint Factor Analysis (JFA) and i-vector based systems including various compensation techniques; Within-Class Covariance Normalization (WCCN), LDA, Scatter Difference Nuisance Attribute Projection (SDNAP) and Gaussian Probabilistic Linear Discriminant Analysis (GPLDA). Speaker verification performance for utterances with as little as 2 sec of data taken from the NIST Speaker Recognition Evaluations are presented to provide a clearer picture of the current performance characteristics of these techniques in short utterance conditions.

Online geographic information systems for improving health planning practice : lessons learned from the case study of Logan-Beaudesert, Australia

This study examines the impact of utilising a Decision Support System (DSS) in a practical health planning study. Specifically, it presents a real-world case of a community-based initiative aiming to improve overall public health outcomes. Previous studies have emphasised that because of a lack of effective information, systems and an absence of frameworks for making informed decisions in health planning, it has become imperative to develop innovative approaches and methods in health planning practice. Online Geographical Information Systems (GIS) has been suggested as one of the innovative methods that will inform decision-makers and improve the overall health planning process. However, a number of gaps in knowledge have been identified within health planning practice: lack of methods to develop these tools in a collaborative manner; lack of capacity to use the GIS application among health decision-makers perspectives, and lack of understanding about the potential impact of such systems on users. This study addresses the abovementioned gaps and introduces an online GIS-based Health Decision Support System (HDSS), which has been developed to improve collaborative health planning in the Logan-Beaudesert region of Queensland, Australia. The study demonstrates a participatory and iterative approach undertaken to design and develop the HDSS. It then explores the perceived user satisfaction and impact of the tool on a selected group of health decision makers. Finally, it illustrates how decision-making processes have changed since its implementation. The overall findings suggest that the online GIS-based HDSS is an effective tool, which has the potential to play an important role in the future in terms of improving local community health planning practice. However, the findings also indicate that decision-making processes are not merely informed by using the HDSS tool. Instead, they seem to enhance the overall sense of collaboration in health planning practice. Thus, to support the Healthy Cities approach, communities will need to encourage decision-making based on the use of evidence, participation and consensus, which subsequently transfers into informed actions.

3D ellipsoid fitting for multi-view gait recognition

Gait recognition approaches continue to struggle with challenges including view-invariance, low-resolution data, robustness to unconstrained environments, and fluctuating gait patterns due to subjects carrying goods or wearing different clothes. Although computationally expensive, model based techniques offer promise over appearance based techniques for these challenges as they gather gait features and interpret gait dynamics in skeleton form. In this paper, we propose a fast 3D ellipsoidal-based gait recognition algorithm using a 3D voxel model derived from multi-view silhouette images. This approach directly solves the limitations of view dependency and self-occlusion in existing ellipse fitting model-based approaches. Voxel models are segmented into four components (left and right legs, above and below the knee), and ellipsoids are fitted to each region using eigenvalue decomposition. Features derived from the ellipsoid parameters are modeled using a Fourier representation to retain the temporal dynamic pattern for classification. We demonstrate the proposed approach using the CMU MoBo database and show that an improvement of 15-20% can be achieved over a 2D ellipse fitting baseline.

Gait energy volumes and frontal gait recognition using depth images

Gait energy images (GEIs) and its variants form the basis of many recent appearance-based gait recognition systems. The GEI combines good recognition performance with a simple implementation, though it suffers problems inherent to appearance-based approaches, such as being highly view dependent. In this paper, we extend the concept of the GEI to 3D, to create what we call the gait energy volume, or GEV. A basic GEV implementation is tested on the CMU MoBo database, showing improvements over both the GEI baseline and a fused multi-view GEI approach. We also demonstrate the efficacy of this approach on partial volume reconstructions created from frontal depth images, which can be more practically acquired, for example, in biometric portals implemented with stereo cameras, or other depth acquisition systems. Experiments on frontal depth images are evaluated on an in-house developed database captured using the Microsoft Kinect, and demonstrate the validity of the proposed approach.

Compressive sensing for gait recognition

Compressive Sensing (CS) is a popular signal processing technique, that can exactly reconstruct a signal given a small number of random projections of the original signal, provided that the signal is sufficiently sparse. We demonstrate the applicability of CS in the field of gait recognition as a very effective dimensionality reduction technique, using the gait energy image (GEI) as the feature extraction process. We compare the CS based approach to the principal component analysis (PCA) and show that the proposed method outperforms this baseline, particularly under situations where there are appearance changes in the subject. Applying CS to the gait features also avoids the need to train the models, by using a generalised random projection.

Caseload management, work-related stress and case manager self-efficacy among Victorian mental health case managers

Objective: In Australia and comparable countries, case management has become the dominant process by which public mental health services provide outpatient clinical services to people with severe mental illness. There is recognition that caseload size impacts on service provision and that management of caseloads is an important dimension of overall service management. There has been little empirical investigation, however, of caseload and its management. The present study was undertaken in the context of an industrial agreement in Victoria, Australia that required services to introduce standardized approaches to caseload management. The aims of the present study were therefore to (i) investigate caseload size and approaches to caseload management in Victoria's mental health services; and (ii) determine whether caseload size and/or approach to caseload management is associated with work-related stress or case manager self-efficacy among community mental health professionals employed in Victoria's mental health services. Method: A total of 188 case managers responded to an online cross-sectional survey with both purpose-developed items investigating methods of case allocation and caseload monitoring, and standard measures of work-related stress and case manager personal efficacy. Results: The mean caseload size was 20 per full-time case manager. Both work-related stress scores and case manager personal efficacy scores were broadly comparable with those reported in previous studies. Higher caseloads were associated with higher levels of work-related stress and lower levels of case manager personal efficacy. Active monitoring of caseload was associated with lower scores for work-related stress and higher scores for case manager personal efficacy, regardless of size of caseload. Although caseloads were most frequently monitored by the case manager, there was evidence that monitoring by a supervisor was more beneficial than self-monitoring. Conclusion: Routine monitoring of caseload, especially by a workplace supervisor, may be effective in reducing work-related stress and enhancing case manager personal efficacy. Keywords: case management, caseload, stress