Collaboration and competition on a Wiki : the praxis of online social learning to improve academic writing and research in under-graduate students

The number of internet users in Australia has been steadily increasing, with over 10.9 million people currently subscribed to an internet provider (ABS, 2011). Over the past year, the most avid users of the Internet were 15 – 24 year olds, with approximately 95% accessing the internet on a regular basis (ABS, Social Trends, 2011). While the internet, in particularly Web 2.0, has been described as fundamental to higher education students, social and leisure internet tools are also increasingly being used by these students to generate and maintain their social and professional networks and interactions (Duffy & Bruns, 2006). Rapid technological advancements have enabled greater and faster access to information for learning and education (Hemmi et al, 2009; Glassman & Kang, 2011). As such, we sought to integrate interactive, online social media into the assessment profile of a Public Health undergraduate cohort at the Queensland University of Technology (QUT). The aim of this exercise was to engage undergraduate students to both develop and showcase their research on a range of complex, contemporary health issues within the online forum of Wikispaces for review and critique by their peers. We applied Bandura’s Social Learning Theory (SLT) to analyse the interactive processes from which students developed deeper and more sustained learning, and via which their overall academic writing standards were enriched. This paper outlines the assessment task, and the students’ feedback on their learning outcomes in relation to the Attentional, Retentional, Motor Reproduction, and Motivational Processes outlined by Bandura in SLT. We conceptualise the findings in a theoretical model, and discuss the implications for this approach within the broader tertiary environment.

Pt/Nanograined ZnO/SiC Schottky diode based hydrogen and propene sensor

A nanostructured Schottky diode was fabricated to sense hydrogen and propene gases in the concentration range of 0.06% to 1%. The ZnO sensitive layer was deposited on SiC substrate by pulse laser deposition technique. Scanning electron microscopy and X-ray diffraction characterisations revealed presence of wurtzite structured ZnO nanograins grown in the direction of (002) and (004). The nanostructured diode was investigated at optimum operating temperature of 260 °C. At a constant reverse current of 1 mA, the voltage shifts towards 1% hydrogen and 1% propene were measured as 173.3 mV and 191.8 mV, respectively.

Overview of the NTCIR-10 cross-lingual link discovery task

This paper presents an overview of NTCIR-10 Cross-lingual Link Discovery (CrossLink-2) task. For the task, we continued using the evaluation framework developed for the NTCIR-9 CrossLink-1 task. Overall, recommended links were evaluated at two levels (file-to-file and anchor-to-file); and system performance was evaluated with metrics: LMAP, R-Prec and P@N.

Two-dimensional graphene heterojunctions: The tunable mechanical properties

We report the mechanical properties of different two-dimensional carbon heterojunctions (HJs) made from graphene and various stable graphene allotropes, including α-, β-, γ- and 6612-graphyne (GY), and graphdiyne (GDY). It is found that all HJs exhibit a brittle behaviour except the one with α-GY, which however shows a hardening process due to the formation of triple carbon rings. Such hardening process has greatly deferred the failure of the structure. The yielding of the HJs is usually initiated at the interface between graphene and graphene allotropes, and monoatomic carbon rings are normally formed after yielding. By varying the locations of graphene (either in the middle or at the two ends of the HJs), similar mechanical properties have been obtained, suggesting insignificant impacts from location of graphene allotropes. Whereas, changing the types and percentages of the graphene allotropes, the HJs exhibit vastly different mechanical properties. In general, with the increasing graphene percentage, the yield strain decreases and the effective Young’s modulus increases. Meanwhile, the yield stress appears irrelevant with the graphene percentage. This study provides a fundamental understanding of the tensile properties of the heterojunctions that are crucial for the design and engineering of their mechanical properties, in order to facilitate their emerging future applications in nanoscale devices, such as flexible/stretchable electronics.

The impact of spatial scales and spatial smoothing on the outcome of Bayesian spatial model

Discretization of a geographical region is quite common in spatial analysis. There have been few studies into the impact of different geographical scales on the outcome of spatial models for different spatial patterns. This study aims to investigate the impact of spatial scales and spatial smoothing on the outcomes of modelling spatial point-based data. Given a spatial point-based dataset (such as occurrence of a disease), we study the geographical variation of residual disease risk using regular grid cells. The individual disease risk is modelled using a logistic model with the inclusion of spatially unstructured and/or spatially structured random effects. Three spatial smoothness priors for the spatially structured component are employed in modelling, namely an intrinsic Gaussian Markov random field, a second-order random walk on a lattice, and a Gaussian field with Matern correlation function. We investigate how changes in grid cell size affect model outcomes under different spatial structures and different smoothness priors for the spatial component. A realistic example (the Humberside data) is analyzed and a simulation study is described. Bayesian computation is carried out using an integrated nested Laplace approximation. The results suggest that the performance and predictive capacity of the spatial models improve as the grid cell size decreases for certain spatial structures. It also appears that different spatial smoothness priors should be applied for different patterns of point data.

Systematic evaluation of GoSoapBox in tertiary education : a student response system for improving learning experiences and outcomes

There is currently a wide range of research into the recent introduction of student response systems in higher education and tertiary settings (Banks 2006; Kay and Le Sange, 2009; Beatty and Gerace 2009; Lantz 2010; Sprague and Dahl 2009). However, most of this pedagogical literature has generated ‘how to’ approaches regarding the use of ‘clickers’, keypads, and similar response technologies. There are currently no systematic reviews on the effectiveness of ‘GoSoapBox’ – a more recent, and increasingly popular student response system – for its capacity to enhance critical thinking, and achieve sustained learning outcomes. With rapid developments in teaching and learning technologies across all undergraduate disciplines, there is a need to obtain comprehensive, evidence-based advice on these types of technologies, their uses, and overall efficacy. This paper addresses this current gap in knowledge. Our teaching team, in an undergraduate Sociology and Public Health unit at the Queensland University of Technology (QUT), introduced GoSoapBox as a mechanism for discussing controversial topics, such as sexuality, gender, economics, religion, and politics during lectures, and to take opinion polls on social and cultural issues affecting human health. We also used this new teaching technology to allow students to interact with each other during class – both on both social and academic topics – and to generate discussions and debates during lectures. The paper reports on a data-driven study into how this interactive online tool worked to improve engagement and the quality of academic work produced by students. This paper will firstly, cover the recent literature reviewing student response systems in tertiary settings. Secondly, it will outline the theoretical framework used to generate this pedagogical research. In keeping with the social and collaborative features of Web 2.0 technologies, Bandura’s Social Learning Theory (SLT) will be applied here to investigate the effectiveness of GoSoapBox as an online tool for improving learning experiences and the quality of academic output by students. Bandura has emphasised the Internet as a tool for ‘self-controlled learning’ (Bandura 2001), as it provides the education sector with an opportunity to reconceptualise the relationship between learning and thinking (Glassman & Kang 2011). Thirdly, we describe the methods used to implement the use of GoSoapBox in our lectures and tutorials, and which aspects of the technology we drew on for learning purposes, as well as the methods for obtaining feedback from the students about the effectiveness or otherwise of this tool. Fourthly, we report cover findings from an examination of all student/staff activity on GoSoapBox as well as reports from students about the benefits and limitations of it as a learning aid. We then display a theoretical model that is produced via an iterative analytical process between SLT and our data analysis for use by academics and teachers across the undergraduate curriculum. The model has implications for all teachers considering the use of student response systems to improve the learning experiences of their students. Finally, we consider some of the negative aspects of GoSoapBox as a learning aid.

Tensile properties of graphene nanotube hybrid structures : a molecular dynamics study

Graphene has been reported with record-breaking properties which have opened up huge potential applications. A considerable research has been devoted to manipulate or modify the properties of graphene to target a more smart nanoscale device. Graphene and carbon nanotube hybrid structure (GNHS) is one of the promising graphene derivates, while their mechanical properties have been rarely discussed in literature. Therefore, such a studied is conducted in this paper basing on the large-scale molecular dynamics simulation. The target GNHS is constructed by considering two separate graphene layers that being connected by single-wall carbon nanotubes (SWCNTs) according to the experimental observations. It is found that the GNHSs exhibit a much lower yield strength, Young’s modulus, and earlier yielding comparing with a bilayer graphene sheet. Fracture of studied GNHSs is found to fracture located at the connecting region between carbon nanotubes (CNTs) and graphene. After failure, monatomic chains are normally observed at the front of the failure region, and the two graphene layers at the failure region without connecting CNTs will adhere to each other, generating a bilayer graphene sheet scheme (with a layer distance about 3.4 Å). This study will enrich the current understanding of the mechanical performance of GNHS, which will guide the design of GNHS and shed lights on its various applications.

Estimation of vibration amplitude in Fourier domain optical coherence tomography interferometric signals from Doppler spectrum

In presented method combination of Fourier and Time domain detection enables to broaden the effective bandwidth for time dependent Doppler Signal that allows for using higher-order Bessel functions to calculate unambiguously the vibration amplitudes.

The mitochondrial genome of the Russian wheat aphid Diuraphis noxia : large repetitive sequences between trnE and trnF in aphids

To characterize aphid mitochondrial genome (mitogenome) features, we sequenced the complete mitogenome of the Russian wheat aphid, Diuraphis noxia. The 15,784-bp mitogenome with a high A + T content (84.76%) and strong C skew (− 0.26) was arranged in the same gene order as that of the ancestral insect. Unlike typical insect mitogenomes, D. noxia possessed a large tandem repeat region (644 bp) located between trnE and trnF. Sequencing partial mitogenome of the cotton aphid (Aphis gossypii) further confirmed the presence of the large repeat region in aphids, but with different repeat length and copy number. Another motif (58 bp) tandemly repeated 2.3 times in the control region of D. noxia. All repeat units in D. noxia could be folded into stem-loop secondary structures, which could further promote an increase in copy numbers. Characterization of the D. noxia mitogenome revealed distinct mitogenome architectures, thus advancing our understanding of insect mitogenomic diversities and evolution.

Identifying steganographic payload location in binary image

In this paper, we propose a steganalysis method that is able to identify the locations of stego bearing pixels in the binary image. In order to do that, our proposed method will calculate the residual between a given stego image and its estimated cover image. After that, we will compute the local entropy difference between these two versions of images as well. Finally, we will compute the mean of residual and mean of local entropy difference across multiple stego images. From these two means, the locations of stego bearing pixels can be identified. The presented empirical results demonstrate that our proposed method can identify the stego bearing locations of near perfect accuracy when sufficient stego images are supplied. Hence, our proposed method can be used to reveal which pixels in the binary image have been used to carry the secret message.

Binary image steganographic techniques classification based on multi-class steganalysis

In this paper, we propose a new multi-class steganalysis for binary image. The proposed method can identify the type of steganographic technique used by examining on the given binary image. In addition, our proposed method is also capable of differentiating an image with hidden message from the one without hidden message. In order to do that, we will extract some features from the binary image. The feature extraction method used is a combination of the method extended from our previous work and some new methods proposed in this paper. Based on the extracted feature sets, we construct our multi-class steganalysis from the SVM classifier. We also present the empirical works to demonstrate that the proposed method can effectively identify five different types of steganography.

Blind steganalysis : a countermeasure for binary image steganography

In this paper, we propose a new blind steganalytic method to detect the presence of secret messages embedded in black and white images using the steganographic techniques. We start by extracting several sets of matrix, such as run length matrix, gap length matrix and pixel difference. We also apply characteristic function on these matrices to enhance their discriminative capabilities. Then we calculate the statistics which include mean, variance, kurtosis and skewness to form our feature sets. The presented empirical works demonstrate our proposed method can effectively detect three different types of steganography. This proves the universality of our proposed method as a blind steganalysis. In addition, the experimental results show our proposed method is capable of detecting small amount of the embedded message.

Estimating hidden message length in binary image embedded by using boundary pixels steganography

In this paper, we propose a new steganalytic method to detect the message hidden in a black and white image using the steganographic technique developed by Liang, Wang and Zhang. Our detection method estimates the length of hidden message embedded in a binary image. Although the hidden message embedded is visually imperceptible, it changes some image statistic (such as inter-pixels correlation). Based on this observation, we first derive the 512 patterns histogram from the boundary pixels as the distinguishing statistic, then we compute the histogram difference to determine the changes of the 512 patterns histogram induced by the embedding operation. Finally we propose histogram quotient to estimate the length of the embedded message. Experimental results confirm that the proposed method can effectively and reliably detect the length of the embedded message.

Features-pooling blind jpeg image steganalysis

In this research, we introduce a new blind steganalysis in detecting grayscale JPEG images. Features-pooling method is employed to extract the steganalytic features and the classification is done by using neural network. Three different steganographic models are tested and classification results are compared to the five state-of-the-art blind steganalysis.

JPEG image steganalysis improvement via image-to-image variation minimization

In this research, we introduce an approach to enhance the discriminative capability of features by employing image-to-image variation minimization. In order to minimize image-to-image variation, we will estimate the cover image from the stego image by decompressing the stego image, transforming the decompressed image and recompressing back. Since the effect of the embedding operation in an image steganography is actually a noise adding process to the image, applying these three processes will smooth out the noise and hence the estimated cover image can be obtained.