Action learning of online marketing via a global contest

Introduction: The Google Online Marketing Challenge is a global competition in which student teams run advertising campaigns for small and medium-sized businesses (SMEs) using AdWords, Google’s text-based advertisements. In 2008, its inaugural year, over 8,000 students and 300 instructors from 47 countries representing over 200 schools participated. The Challenge ran in undergraduate and graduate classes in disciplines such as marketing, tourism, advertising, communication and information systems. Combining advertising and education, the Challenge gives student hands-on experience in the increasingly important field of online marketing, engages them with local businesses and motivates them through the thrill of a global competition. Student teams receive US$200 in AdWords credits, Google’s premier advertising product that offers cost-per-click advertisements. The teams then recruit and work with a local business to devise an effective online marketing campaign. Students first outline a strategy, run a series of campaigns, and provide their business with recommendations to improve their online marketing. Teams submit two written reports for judging by 14 academics in eight countries. In addition, Google AdWords experts judge teams on their campaign statistics such as success metrics and account management. Rather than a marketing simulation against a computer or hypothetical marketing plans for hypothetical businesses, the Challenges has student teams develop and manage real online advertising campaigns for their clients and compete against peers globally.

Cardiac state diagnosis using higher order spectra of heart rate variability

Heart rate variability (HRV) refers to the regulation of the sinoatrial node, the natural pacemaker of the heart, by the sympathetic and parasympathetic branches of the autonomic nervous system. Heart rate variability analysis is an important tool to observe the heart's ability to respond to normal regulatory impulses that affect its rhythm. A computer-based intelligent system for analysis of cardiac states is very useful in diagnostics and disease management. Like many bio-signals, HRV signals are nonlinear in nature. Higher order spectral analysis (HOS) is known to be a good tool for the analysis of nonlinear systems and provides good noise immunity. In this work, we studied the HOS of the HRV signals of normal heartbeat and seven classes of arrhythmia. We present some general characteristics for each of these classes of HRV signals in the bispectrum and bicoherence plots. We also extracted features from the HOS and performed an analysis of variance (ANOVA) test. The results are very promising for cardiac arrhythmia classification with a number of features yielding a p-value < 0.02 in the ANOVA test.

Improving automatic speaker verification using SVM techniques

Automatic recognition of people is an active field of research with important forensic and security applications. In these applications, it is not always possible for the subject to be in close proximity to the system. Voice represents a human behavioural trait which can be used to recognise people in such situations. Automatic Speaker Verification (ASV) is the process of verifying a persons identity through the analysis of their speech and enables recognition of a subject at a distance over a telephone channel { wired or wireless. A significant amount of research has focussed on the application of Gaussian mixture model (GMM) techniques to speaker verification systems providing state-of-the-art performance. GMM's are a type of generative classifier trained to model the probability distribution of the features used to represent a speaker. Recently introduced to the field of ASV research is the support vector machine (SVM). An SVM is a discriminative classifier requiring examples from both positive and negative classes to train a speaker model. The SVM is based on margin maximisation whereby a hyperplane attempts to separate classes in a high dimensional space. SVMs applied to the task of speaker verification have shown high potential, particularly when used to complement current GMM-based techniques in hybrid systems. This work aims to improve the performance of ASV systems using novel and innovative SVM-based techniques. Research was divided into three main themes: session variability compensation for SVMs; unsupervised model adaptation; and impostor dataset selection. The first theme investigated the differences between the GMM and SVM domains for the modelling of session variability | an aspect crucial for robust speaker verification. Techniques developed to improve the robustness of GMMbased classification were shown to bring about similar benefits to discriminative SVM classification through their integration in the hybrid GMM mean supervector SVM classifier. Further, the domains for the modelling of session variation were contrasted to find a number of common factors, however, the SVM-domain consistently provided marginally better session variation compensation. Minimal complementary information was found between the techniques due to the similarities in how they achieved their objectives. The second theme saw the proposal of a novel model for the purpose of session variation compensation in ASV systems. Continuous progressive model adaptation attempts to improve speaker models by retraining them after exploiting all encountered test utterances during normal use of the system. The introduction of the weight-based factor analysis model provided significant performance improvements of over 60% in an unsupervised scenario. SVM-based classification was then integrated into the progressive system providing further benefits in performance over the GMM counterpart. Analysis demonstrated that SVMs also hold several beneficial characteristics to the task of unsupervised model adaptation prompting further research in the area. In pursuing the final theme, an innovative background dataset selection technique was developed. This technique selects the most appropriate subset of examples from a large and diverse set of candidate impostor observations for use as the SVM background by exploiting the SVM training process. This selection was performed on a per-observation basis so as to overcome the shortcoming of the traditional heuristic-based approach to dataset selection. Results demonstrate the approach to provide performance improvements over both the use of the complete candidate dataset and the best heuristically-selected dataset whilst being only a fraction of the size. The refined dataset was also shown to generalise well to unseen corpora and be highly applicable to the selection of impostor cohorts required in alternate techniques for speaker verification.

Reducing high levels of communication apprehension among primary school students

This study focused on a group of primary school teachers as they implemented a variety of intervention actions within their class programs aimed towards supporting the reduction of high levels of communication apprehension (CA) among students.Six teachers and nine students, located across three primary schools, four year levels,and six classes, participated in this study. For reasons of confidentiality the schools,principals, parents, teachers, teacher assistants, and students who were involved in this study were given fictitious names. The following research question was explored in this study: What intervention actions can primary school teachers implement within their class programs that support the reduction of high CA levels among students? Throughout this study the term CA referred to "an individual's level of fear or anxiety associated with either real or anticipated (oral) communication with another person or persons" (McCroskey, 1984, p. 13). The sources of CA were explained with reference to McCroskey's state-trait continuum. The distinctions between high and appropriate levels of CA were determined conceptually and empirically. The education system within which this study was conducted promoted the philosophy of inclusion and the practices of inclusive schooling. Teachers employed in this system were encouraged to create class programs inclusive of and successful for all students. Consequently the conceptual framework within which this study was conducted was based around the notion of inclusion. Action research and case study research were the methodologies used in the study. Case studies described teachers' action research as they responded to the challenge of executing intervention actions within their class programs directed towards supporting the reduction of high CA levels among students. Consequently the teachers and not the researcher were the central characters in each of the case studies. Three principal data collection instruments were used in this study: Personal Report of Communication Fear (PRCF) scale, semistructured interviews, and dialogue journals. The PRCF scale was the screening tool used to identify a pool of students eligible for the study. Data relevant to the students involved in the study were gathered during semistructured interviews and throughout the dialogue journaling process. Dialogue journaling provided the opportunity for regular contact between teachers and the researcher, a sequence to teacher and student intervention behaviours, and a permanent record of teacher and student growth and development. The majority of teachers involved in this study endeavoured to develop class programs inclusive of all students.These teachers acknowledged the importance of modifying aspects of their class programs in response to the diverse and often multiple needs of individual students with high levels of CA. Numerous conclusions were drawn regarding practical ways that the teachers in this study supported the reduction of high CA levels among students. What this study has shown is that teachers can incorporate intervention actions within their class programs aimed towards supporting students lower their high levels of CA. Whilst no teacher developed an identical approach to intervention, similarities and differences were evident among teachers regarding their selection, interpretation, and implementation of intervention actions. Actions that teachers enacted within their class programs emerged from numerous fields of research including CA, inclusion, social skills, behaviour teaching, co-operative learning, and quality schools. Each teacher's knowledge of and familiarity with these research fields influenced their preference for and commitment to particular intervention actions. Additional factors including each teacher's paradigm of inclusion and exclusion contributed towards their choice of intervention actions. Possible implications of these conclusions were noted with reference to teachers,school administrators, support personnel, system personnel, teacher educators, parents, and researchers.

A parallel computation of power system equations

Streaming SIMD Extensions (SSE) is a unique feature embedded in the Pentium III and P4 classes of microprocessors. By fully exploiting SSE, parallel algorithms can be implemented on a standard personal computer and a theoretical speedup of four can be achieved. In this paper, we demonstrate the implementation of a parallel LU matrix decomposition algorithm for solving power systems network equations with SSE and discuss advantages and disadvantages of this approach.

A parallel solution to linear systems

Streaming SIMD Extensions (SSE) is a unique feature embedded in the Pentium III and IV classes of microprocessors. By fully exploiting SSE, parallel algorithms can be implemented on a standard personal computer and a theoretical speedup of four can be achieved. In this paper, we demonstrate the implementation of a parallel LU matrix decomposition algorithm for solving linear systems with SSE and discuss advantages and disadvantages of this approach based on our experimental study.

Parallel solution for railway power network simulation

The Streaming SIMD extension (SSE) is a special feature that is available in the Intel Pentium III and P4 classes of microprocessors. As its name implies, SSE enables the execution of SIMD (Single Instruction Multiple Data) operations upon 32-bit floating-point data therefore, performance of floating-point algorithms can be improved. In electrified railway system simulation, the computation involves the solving of a huge set of simultaneous linear equations, which represent the electrical characteristic of the railway network at a particular time-step and a fast solution for the equations is desirable in order to simulate the system in real-time. In this paper, we present how SSE is being applied to the railway network simulation.

Towards learner autonomy : an assessment for learning approach

Using Assessment for Learning (AfL) may develop learner autonomy however, very often AfL is reduced to a set of strategies that do not always achieve the desired outcome. This research adopted a different approach that examined AfL as a cultural practice, situated within influential social relationships that shape learner identity. The study addressed the question “What are the qualities of the teacher-student relationship that support student learning autonomy in an AfL context?” Three case studies of the interactions of Queensland middle school teachers and their classes of Year 7, 8 and 9 were developed over one year. Data were collected from field notes and video recordings of classroom interactions and individual and focus group interviews with teachers and students. The analysis began with a close look at the field data. Interpretations that emerged from a sociocultural theoretical understanding were helpful in informing the process of analysis. Themes and patterns of interrelationships were identified through thematic coding using a constant comparative approach. Validation was achieved through methodological triangulation. Four findings that inform an understanding of AfL and the development of learner autonomy emerged. Firstly, autonomy is theorised as a context-specific identity mediated through the teacher-student relationship. Secondly, it was observed that learners negotiated their identities as knowers through AfL practices in various tacit, explicit, group and individual ways in a ‘generative dance’ of knowing in action (Cook & Brown, 2005). Thirdly, teachers and learners negotiated their participation by drawing from identities in multiple communities of practice. Finally it is proposed that a new participative identity or narrative for assessment is needed. This study contributes to understandings about teacher AfL practices that can help build teacher assessment capacity. Importantly, autonomy is understood as an identity that is available to all learners. This study is also significant as it affirms the importance of teacher assessment to support learners in developing autonomy, a focus that challenges the singular assessment policy focus on measuring performance. Finally this study contributes to a sociocultural theoretical understanding of AfL.

Frock Paper Scissors [website]

Frock Paper Sissors (http://www.frockpaperscissors.com): curated web based fashion work. Research has focussed on creating a professional and ‘real world’ website (available in the international/public arena) while producing a high quality design and journalistic fashion medium. The hard copy Frock Paper Scissors magazine has been the focus of assessment in a Fashion and Style Journalism class for the last five years, and for the last three years, students from an Advanced Web Design class have been involved in the production of the accompanying web site, http://www.frockpapersissors.com. This project researches the ways in which synergies across design disciplines can be developed through student engagement on authentic design projects. The Frock Paper Scissors website is a curated collaboration of work from the Fashion,Journalism, Creative Industries and Communication Design discipline areas in the Creative Industries Faculty at Queensland University of Technology (QUT). Research focusses on how this authentic assessment task has been integrated into the two design (and communication)classes; discussing the different approaches taken by teaching staff, the challenges faced, and the ways in which student learning outcomes have been improved through interactions between design disciplines. The final curated work is a public/international website which successfully displays student work and engages students from different design (and creative industries) fields on an authentic design project within their studies.

Psychotropic drugs

19.1 Depression and Antidepressants 19.1.1 Depression 19.1.2 Neurochemistry of Depression and the Monoamine Theory 19.1.3 Antidepressant Indications and Drug Classes 19.1.4 General Considerations with the use of Antidepressants 19.1.5 Tricyclic Antidepressants 19.1.6 Monoamine Oxidase Inhibitors 19.1.7 Selective Serotonin Reuptake Inhibitors 19.1.8 Combined Serotonin and Noradrenaline Reuptake Inhibitors 19.1.9 Long Term Adaptive Changes with Antidepressants 19.2 Psychosis, Schizophrenia, and Antipsychotics 19.2.1 Psychosis and Schizophrenia 19.2.2 Neurochemistry of Psychosis and the Dopamine Theory 19.2.3 Antipsychotic Drug Indications and Drug Classes 19.2.4 Antipsychotic Mechanisms of Action 19.2.5 Typical Antipsychotics (First Generation) 19.2.6 Atypical Antipsychotics (Second Generation) 19.3 Anxiety and Anxiolytics 19.3.1 Fear, Anxiety and Anxiety Disorders 19.3.2 Neurochemistry of Anxiety 19.3.3 Anxiolytic Drug Indications and Drug Classes 19.3.4 Benzodiazepines 19.3.5 Antidepressants 19.3.6 Buspirone

Breast cancer detection from thermal images using bispectral invariant features

Highly sensitive infrared (IR) cameras provide high-resolution diagnostic images of the temperature and vascular changes of breasts. These images can be processed to emphasize hot spots that exhibit early and subtle changes owing to pathology. The resulting images show clusters that appear random in shape and spatial distribution but carry class dependent information in shape and texture. Automated pattern recognition techniques are challenged because of changes in location, size and orientation of these clusters. Higher order spectral invariant features provide robustness to such transformations and are suited for texture and shape dependent information extraction from noisy images. In this work, the effectiveness of bispectral invariant features in diagnostic classification of breast thermal images into malignant, benign and normal classes is evaluated and a phase-only variant of these features is proposed. High resolution IR images of breasts, captured with measuring accuracy of ±0.4% (full scale) and temperature resolution of 0.1 °C black body, depicting malignant, benign and normal pathologies are used in this study. Breast images are registered using their lower boundaries, automatically extracted using landmark points whose locations are learned during training. Boundaries are extracted using Canny edge detection and elimination of inner edges. Breast images are then segmented using fuzzy c-means clustering and the hottest regions are selected for feature extraction. Bispectral invariant features are extracted from Radon projections of these images. An Adaboost classifier is used to select and fuse the best features during training and then classify unseen test images into malignant, benign and normal classes. A data set comprising 9 malignant, 12 benign and 11 normal cases is used for evaluation of performance. Malignant cases are detected with 95% accuracy. A variant of the features using the normalized bispectrum, which discards all magnitude information, is shown to perform better for classification between benign and normal cases, with 83% accuracy compared to 66% for the original.

The use of artificial MicroRNA technology to control gene expression in Arabidopsis thaliana

In plants, double-stranded RNA (dsRNA) is an effective trigger of RNA silencing, and several classes of endogenous small RNA (sRNA), processed from dsRNA substrates by DICER-like (DCL) endonucleases, are essential in controlling gene expression. One such sRNA class, the microRNAs (miRNAs) control the expression of closely related genes to regulate all aspects of plant development, including the determination of leaf shape, leaf polarity, flowering time, and floral identity. A single miRNA sRNA silencing signal is processed from a long precursor transcript of nonprotein-coding RNA, termed the primary miRNA (pri-miRNA). A region of the pri-miRNA is partially self-complementary allowing the transcript to fold back onto itself to form a stem-loop structure of imperfectly dsRNA. Artificial miRNA (amiRNA) technology uses endogenous pri-miRNAs, in which the miRNA and miRNA*(passenger strand of the miRNA duplex) sequences have been replaced with corresponding amiRNA/ amiRNA*sequences that direct highly efficient RNA silencing of the targeted gene. Here, we describe the rules for amiRNA design, as well as outline the PCR and bacterial cloning procedures involved in the construction of an amiRNA plant expression vector to control target gene expression in Arabidopsis thaliana. © 2014 Springer Science+Business Media New York.

Predicting fault-prone software modules with rank sum classification

The detection and correction of defects remains among the most time consuming and expensive aspects of software development. Extensive automated testing and code inspections may mitigate their effect, but some code fragments are necessarily more likely to be faulty than others, and automated identification of fault prone modules helps to focus testing and inspections, thus limiting wasted effort and potentially improving detection rates. However, software metrics data is often extremely noisy, with enormous imbalances in the size of the positive and negative classes. In this work, we present a new approach to predictive modelling of fault proneness in software modules, introducing a new feature representation to overcome some of these issues. This rank sum representation offers improved or at worst comparable performance to earlier approaches for standard data sets, and readily allows the user to choose an appropriate trade-off between precision and recall to optimise inspection effort to suit different testing environments. The method is evaluated using the NASA Metrics Data Program (MDP) data sets, and performance is compared with existing studies based on the Support Vector Machine (SVM) and Naïve Bayes (NB) Classifiers, and with our own comprehensive evaluation of these methods.

Drosophila 3′ UTRs are more complex than protein-coding sequences

The 3′ UTRs of eukaryotic genes participate in a variety of post-transcriptional (and some transcriptional) regulatory interactions. Some of these interactions are well characterised, but an undetermined number remain to be discovered. While some regulatory sequences in 3′ UTRs may be conserved over long evolutionary time scales, others may have only ephemeral functional significance as regulatory profiles respond to changing selective pressures. Here we propose a sensitive segmentation methodology for investigating patterns of composition and conservation in 3′ UTRs based on comparison of closely related species. We describe encodings of pairwise and three-way alignments integrating information about conservation, GC content and transition/transversion ratios and apply the method to three closely related Drosophila species: D. melanogaster, D. simulans and D. yakuba. Incorporating multiple data types greatly increased the number of segment classes identified compared to similar methods based on conservation or GC content alone. We propose that the number of segments and number of types of segment identified by the method can be used as proxies for functional complexity. Our main finding is that the number of segments and segment classes identified in 3′ UTRs is greater than in the same length of protein-coding sequence, suggesting greater functional complexity in 3′ UTRs. There is thus a need for sustained and extensive efforts by bioinformaticians to delineate functional elements in this important genomic fraction. C code, data and results are available upon request.

Automated diagnosis of Age-related Macular Degeneration using greyscale features from digital fundus images

Age-related Macular Degeneration (AMD) is one of the major causes of vision loss and blindness in ageing population. Currently, there is no cure for AMD, however early detection and subsequent treatment may prevent the severe vision loss or slow the progression of the disease. AMD can be classified into two types: dry and wet AMDs. The people with macular degeneration are mostly affected by dry AMD. Early symptoms of AMD are formation of drusen and yellow pigmentation. These lesions are identified by manual inspection of fundus images by the ophthalmologists. It is a time consuming, tiresome process, and hence an automated diagnosis of AMD screening tool can aid clinicians in their diagnosis significantly. This study proposes an automated dry AMD detection system using various entropies (Shannon, Kapur, Renyi and Yager), Higher Order Spectra (HOS) bispectra features, Fractional Dimension (FD), and Gabor wavelet features extracted from greyscale fundus images. The features are ranked using t-test, Kullback–Lieber Divergence (KLD), Chernoff Bound and Bhattacharyya Distance (CBBD), Receiver Operating Characteristics (ROC) curve-based and Wilcoxon ranking methods in order to select optimum features and classified into normal and AMD classes using Naive Bayes (NB), k-Nearest Neighbour (k-NN), Probabilistic Neural Network (PNN), Decision Tree (DT) and Support Vector Machine (SVM) classifiers. The performance of the proposed system is evaluated using private (Kasturba Medical Hospital, Manipal, India), Automated Retinal Image Analysis (ARIA) and STructured Analysis of the Retina (STARE) datasets. The proposed system yielded the highest average classification accuracies of 90.19%, 95.07% and 95% with 42, 54 and 38 optimal ranked features using SVM classifier for private, ARIA and STARE datasets respectively. This automated AMD detection system can be used for mass fundus image screening and aid clinicians by making better use of their expertise on selected images that require further examination.