Engaging undergraduate nursing students in face-to-face tutorials

Chronic nursing shortages have placed increasing pressure on many nursing schools to recruit greater numbers of students with the consequence of larger class sizes. Larger class sizes have the potential to lead to student disengagement. This paper describes a case study that examined the strategies used by a group of nursing lecturers to engage students and to overcome passivity in a Bachelor of Nursing programme. A non-participant observer attended 20 tutorials to observe five academics deliver four tutorials each. Academics were interviewed both individually and as a group following the completion of all tutorial observations. All observations, field notes, interviews and focus groups were coded separately and major themes identified. From this analysis two broad categories emerged: getting students involved; and engagement as a struggle. Academics used a wide variety of techniques to interest and involve students. Additionally, academics desired an equal relationship with students. They believed that both they and the students had some power to influence the dynamics of tutorials and that neither party had ultimate power. The findings of this study serve to re-emphasise past literature which suggests that to engage students, the academics must also engage.

Nonprofit organisations face a restructuring state : the case of local social services in the Hunter region of New South Wales

This paper is concerned with certain of the characteristics of local social services, and their role in a restructuring Australian welfare state. I am particularly concerned with the distinctive gender characteristics of these organisations, because in comparison with most other organisations they have a feminised quality. This partly mirrors women's traditional role of undertaking the major part of the caring labour of society. However, simultaneously work in these organisation deviates from more traditional patterns where employed women occupy subordinate positions. In many community organisations, women occupy leadership roles. The analysis here is concerned with the apparently paradoxical nature of these organisations in their capacity to entrench traditional gender roles and to challenge these by allowing women to fill management positions. It is also concerned to examine whether changes that have been occurring in the community services sector over the last two decades are likely to enhance women's general position in the society, or diminish the power exercised by women. The paper draws in a preliminary way on a study of local services in the Hunter Region of NSW undertaken in the latter half of 1992. These preliminary findings are set against the broader picture of developments in the contemporary welfare state.

Anchored deformable face ensemble alignment

At present, many approaches have been proposed for deformable face alignment with varying degrees of success. However, the common drawback to nearly all these approaches is the inaccurate landmark registrations. The registration errors which occur are predominantly heterogeneous (i.e. low error for some frames in a sequence and higher error for others). In this paper we propose an approach for simultaneously aligning an ensemble of deformable face images stemming from the same subject given noisy heterogeneous landmark estimates. We propose that these initial noisy landmark estimates can be used as an “anchor” in conjunction with known state-of-the-art objectives for unsupervised image ensemble alignment. Impressive alignment performance is obtained using well known deformable face fitting algorithms as “anchors.

Challenges for researchers investigating contraceptive use and pregnancy intentions of young women living in urban and rural Australia : face-to-face discussions to increase participation in a web-based survey

Background: It is imperative to understand how to engage young women in research about issues that are important to them. There is limited reliable data on how young women access contraception in Australia especially in rural areas where services may be less available. Objective: This paper identifies the challenges involved in engaging young Australian women aged 18-23 years to participate in a web-based survey on contraception and pregnancy and ensure their ongoing commitment to follow-up web-based surveys. Methods: A group of young women, aged 18-23 years and living in urban and rural New South Wales, Australia, were recruited to participate in face-to-face discussions using several methods of recruitment: direct contact (face-to-face, telephone or email)and snowball sampling by potential participants inviting their friends. All discussions were transcribed verbatim and analyzed using thematic analysis. Results: Twenty young women participated (urban, n=10: mean age 21.6 years; rural, n=10: 20.0 years) and all used computers or smart phones to access the internet on a daily basis. All participants were concerned about the cost of internet access and utilized free access to social media on their mobile phones. Their willingness to participate in a web-based survey was dependent on incentives with a preference for small financial rewards. Most participants were concerned about their personal details and survey responses remaining confidential and secure. The most appropriate survey would take up to 15 minutes to complete, be a mix of short and long questions and eye-catching with bright colours. Questions on the sensitive topics of sexual activity, contraception and pregnancy were acceptable if they could respond with “I prefer not to answer”. Conclusions: There are demographic, participation and survey design challenges in engaging young women in a web-based survey. Based on our findings, future research efforts are needed to understand the full extent of the role social media and incentives play in the decision of young women to participate in web-based research.

Quality based frame selection for video face recognition

Quality based frame selection is a crucial task in video face recognition, to both improve the recognition rate and to reduce the computational cost. In this paper we present a framework that uses a variety of cues (face symmetry, sharpness, contrast, closeness of mouth, brightness and openness of the eye) to select the highest quality facial images available in a video sequence for recognition. Normalized feature scores are fused using a neural network and frames with high quality scores are used in a Local Gabor Binary Pattern Histogram Sequence based face recognition system. Experiments on the Honda/UCSD database shows that the proposed method selects the best quality face images in the video sequence, resulting in improved recognition performance.

Cross layer optimization of VANET routing with multi-objective decision making

Vehicular Ad-hoc Networks (VANET) have different characteristics compared to other mobile ad-hoc networks. The dynamic nature of the vehicles which act as routers and clients are connected with unreliable radio links and Routing becomes a complex problem. First we propose CO-GPSR (Cooperative GPSR), an extension of the traditional GPSR (Greedy Perimeter Stateless Routing) which uses relay nodes which exploit radio path diversity in a vehicular network to increase routing performance. Next we formulate a Multi-objective decision making problem to select optimum packet relaying nodes to increase the routing performance further. We use cross layer information for the optimization process. We evaluate the routing performance more comprehensively using realistic vehicular traces and a Nakagami fading propagation model optimized for highway scenarios in VANETs. Our results show that when Multi-objective decision making is used for cross layer optimization of routing a 70% performance increment can be obtained for low vehicle densities on average, which is a two fold increase compared to the single criteria maximization approach.

A study on the effects of routing symbol design on process model comprehension

Process modeling grammars are used to create models of business processes. In this paper, we discuss how different routing symbol designs affect an individual's ability to comprehend process models. We conduct an experiment with 154 students to ascertain which visual design principles influence process model comprehension. Our findings suggest that design principles related to perceptual discriminability and pop out improve comprehension accuracy. Furthermore, semantic transparency and aesthetic design of symbols lower the perceived difficulty of comprehension. Our results inform important principles about notational design of process modeling grammars and the effective use of process modeling in practice.

Deformable face ensemble alignment with robust grouped-L1 anchors

Many methods exist at the moment for deformable face fitting. A drawback to nearly all these approaches is that they are (i) noisy in terms of landmark positions, and (ii) the noise is biased across frames (i.e. the misalignment is toward common directions across all frames). In this paper we propose a grouped $\mathcal{L}1$-norm anchored method for simultaneously aligning an ensemble of deformable face images stemming from the same subject, given noisy heterogeneous landmark estimates. Impressive alignment performance improvement and refinement is obtained using very weak initialization as "anchors".

Efficient real-time face detection for high resolution surveillance applications

This paper presents an efficient face detection method suitable for real-time surveillance applications. Improved efficiency is achieved by constraining the search window of an AdaBoost face detector to pre-selected regions. Firstly, the proposed method takes a sparse grid of sample pixels from the image to reduce whole image scan time. A fusion of foreground segmentation and skin colour segmentation is then used to select candidate face regions. Finally, a classifier-based face detector is applied only to selected regions to verify the presence of a face (the Viola-Jones detector is used in this paper). The proposed system is evaluated using 640 x 480 pixels test images and compared with other relevant methods. Experimental results show that the proposed method reduces the detection time to 42 ms, where the Viola-Jones detector alone requires 565 ms (on a desktop processor). This improvement makes the face detector suitable for real-time applications. Furthermore, the proposed method requires 50% of the computation time of the best competing method, while reducing the false positive rate by 3.2% and maintaining the same hit rate.

Efficient route update and maintenance processes for multipath routing in large-scale industrial wireless sensor networks

Reliable communications is one of the major concerns in wireless sensor networks (WSNs). Multipath routing is an effective way to improve communication reliability in WSNs. However, most of existing multipath routing protocols for sensor networks are reactive and require dynamic route discovery. If there are many sensor nodes from a source to a destination, the route discovery process will create a long end-to-end transmission delay, which causes difficulties in some time-critical applications. To overcome this difficulty, the efficient route update and maintenance processes are proposed in this paper. It aims to limit the amount of routing overhead with two-tier routing architecture and introduce the combination of piggyback and trigger update to replace the periodic update process, which is the main source of unnecessary routing overhead. Simulations are carried out to demonstrate the effectiveness of the proposed processes in improvement of total amount of routing overhead over existing popular routing protocols.

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.

Effects of air pollution exposure on adult bicycle commuters : an investigation of respiratory health, motorised traffic proximity and the utility of commute re-routing

Bicycle commuting has the potential to be an effective contributing solution to address some of modern society’s biggest issues, including cardiovascular disease, anthropogenic climate change and urban traffic congestion. However, individuals shifting from a passive to an active commute mode may be increasing their potential for air pollution exposure and the associated health risk. This project, consisting of three studies, was designed to investigate the health effects of bicycle commuters in relation to air pollution exposure, in a major city in Australia (Brisbane). The aims of the three studies were to: 1) examine the relationship of in-commute air pollution exposure perception, symptoms and risk management; 2) assess the efficacy of commute re-routing as a risk management strategy by determining the exposure potential profile of ultrafine particles along commute route alternatives of low and high proximity to motorised traffic; and, 3) evaluate the feasibility of implementing commute re-routing as a risk management strategy by monitoring ultrafine particle exposure and consequential physiological response from using commute route alternatives based on real-world circumstances; 3) investigate the potential of reducing exposure to ultrafine particles (UFP; < 0.1 µm) during bicycle commuting by lowering proximity to motorised traffic with real-time air pollution and acute inflammatory measurements in healthy individuals using their typical, and an alternative to their typical, bicycle commute route. The methods of the three studies included: 1) a questionnaire-based investigation with regular bicycle commuters in Brisbane, Australia. Participants (n = 153; age = 41 ± 11 yr; 28% female) reported the characteristics of their typical bicycle commute, along with exposure perception and acute respiratory symptoms, and amenability for using a respirator or re-routing their commute as risk management strategies; 2) inhaled particle counts measured along popular pre-identified bicycle commute route alterations of low (LOW) and high (HIGH) motorised traffic to the same inner-city destination at peak commute traffic times. During commute, real-time particle number concentration (PNC; mostly in the UFP range) and particle diameter (PD), heart and respiratory rate, geographical location, and meteorological variables were measured. To determine inhaled particle counts, ventilation rate was calculated from heart-rate-ventilation associations, produced from periodic exercise testing; 3) thirty-five healthy adults (mean ± SD: age = 39 ± 11 yr; 29% female) completed two return trips of their typical route (HIGH) and a pre-determined altered route of lower proximity to motorised traffic (LOW; determined by the proportion of on-road cycle paths). Particle number concentration (PNC) and diameter (PD) were monitored in real-time in-commute. Acute inflammatory indices of respiratory symptom incidence, lung function and spontaneous sputum (for inflammatory cell analyses) were collected immediately pre-commute, and one and three hours post-commute. The main results of the three studies are that: 1) healthy individuals reported a higher incidence of specific acute respiratory symptoms in- and post- (compared to pre-) commute (p < 0.05). The incidence of specific acute respiratory symptoms was significantly higher for participants with respiratory disorder history compared to healthy participants (p < 0.05). The incidence of in-commute offensive odour detection, and the perception of in-commute air pollution exposure, was significantly lower for participants with smoking history compared to healthy participants (p < 0.05). Females reported significantly higher incidence of in-commute air pollution exposure perception and other specific acute respiratory symptoms, and were more amenable to commute re-routing, compared to males (p < 0.05). Healthy individuals have indicated a higher incidence of acute respiratory symptoms in- and post- (compared to pre-) bicycle commuting, with female gender and respiratory disorder history indicating a comparably-higher susceptibility; 2) total mean PNC of LOW (compared to HIGH) was reduced (1.56 x e4 ± 0.38 x e4 versus 3.06 x e4 ± 0.53 x e4 ppcc; p = 0.012). Total estimated ventilation rate did not vary significantly between LOW and HIGH (43 ± 5 versus 46 ± 9 L•min; p = 0.136); however, due to total mean PNC, accumulated inhaled particle counts were 48% lower in LOW, compared to HIGH (7.6 x e8 ± 1.5 x e8 versus 14.6 x e8 ± 1.8 x e8; p = 0.003); 3) LOW resulted in a significant reduction in mean PNC (1.91 x e4 ± 0.93 x e4 ppcc vs. 2.95 x e4 ± 1.50 x e4 ppcc; p ≤ 0.001). Commute distance and duration were not significantly different between LOW and HIGH (12.8 ± 7.1 vs. 12.0 ± 6.9 km and 44 ± 17 vs. 42 ± 17 mins, respectively). Besides incidence of in-commute offensive odour detection (42 vs. 56 %; p = 0.019), incidence of dust and soot observation (33 vs. 47 %; p = 0.038) and nasopharyngeal irritation (31 vs. 41 %; p = 0.007), acute inflammatory indices were not significantly associated to in-commute PNC, nor were these indices reduced with LOW compared to HIGH. The main conclusions of the three studies are that: 1) the perception of air pollution exposure levels and the amenability to adopt exposure risk management strategies where applicable will aid the general population in shifting from passive, motorised transport modes to bicycle commuting; 2) for bicycle commuting at peak morning commute times, inhaled particle counts and therefore cardiopulmonary health risk may be substantially reduced by decreasing exposure to motorised traffic, which should be considered by both bicycle commuters and urban planners; 3) exposure to PNC, and the incidence of offensive odour and nasopharyngeal irritation, can be significantly reduced when utilising a strategy of lowering proximity to motorised traffic whilst bicycle commuting, without significantly increasing commute distance or duration, which may bring important benefits for both healthy and susceptible individuals. In summary, the findings from this project suggests that bicycle commuters can significantly lower their exposure to ultrafine particle emissions by varying their commute route to reduce proximity to motorised traffic and associated combustion emissions without necessarily affecting their time of commute. While the health endpoints assessed with healthy individuals were not indicative of acute health detriment, individuals with pre-disposing physiological-susceptibility may benefit considerably from this risk management strategy – a necessary research focus with the contemporary increased popularity of both promotion and participation in bicycle commuting.