A study of children’s musical preference: A data mining approach

Musical preference has long been a research interest in the field of music education, and studies consistently confirm the importance of musical preference in one’s musical learning experiences. However, only a limited number of studies have been focussed on the field of early childhood education (e.g., Hargreaves, North, & Tarrant, 2006; Roulston, 2006). Further, among these limited early childhood studies, few of them discuss children’s musical preference in both the East and the West. There is very limited literature (e.g., Faulkner et al., 2010; Szymanska, 2012) which explores the data by using a data mining approach. This study aims to bridge the research gaps by examining children’s musical preference in Hong Kong and in South Australia by applying a data mining technique – Self Organising Maps (SOM), which is a clustering method that groups similar data objects together. The application of SOM is new in the field of early childhood education and also in the study of children’s musical preference. This paper specifically aims to expand a previous study (Yim & Ebbeck, 2009) by conducting deeper investigations into the existing datasets, for the purpose of uncovering insights that have not been identified through data mining approach

Operationalising Analytics for Action: A Conceptual Framework Linking Embedded Analytics with Decision-Making Agility

Organisations are increasingly practising Business analytics (BA) to make data-driven business decisions amidst environmental complexities and fierce global competition. However, organisations find it challenging to operationalise BA outputs (such as analytical models, reports, and visualization) primarily due to a lack of (a) integrated technology, (b) collaboration and (c) governance. These factors inhibit organisations’ ability to make data-driven decisions in an agile manner. Embedded analytics, an emerging BA practice, has the potential to address these issues by integrating BA outputs into business applications and workflows, thereby promoting the culture of data-driven decision-making. In this research-in-progress paper, we integrate the diverse areas of literature on BA, embedded analytics, and dynamic capabilities theory and propose a research model that links embedded analytics to decision-making agility through the development of dynamic capabilities. The details of the framework highlight how organisations can get maximum value from data and analytics initiatives through operationalisation of BA outputs

Feature selection for high dimensional imbalanced class data using harmony search

Misclassification costs of minority class data in real-world applications can be very high. This is a challenging problem especially when the data is also high in dimensionality because of the increase in overfitting and lower model interpretability. Feature selection is recently a popular way to address this problem by identifying features that best predict a minority class. This paper introduces a novel feature selection method call SYMON which uses symmetrical uncertainty and harmony search. Unlike existing methods, SYMON uses symmetrical uncertainty to weigh features with respect to their dependency to class labels. This helps to identify powerful features in retrieving the least frequent class labels. SYMON also uses harmony search to formulate the feature selection phase as an optimisation problem to select the best possible combination of features. The proposed algorithm is able to deal with situations where a set of features have the same weight, by incorporating two vector tuning operations embedded in the harmony search process. In this paper, SYMON is compared against various benchmark feature selection algorithms that were developed to address the same issue. Our empirical evaluation on different micro-array data sets using G-Mean and AUC measures confirm that SYMON is a comparable or a better solution to current benchmarks

A comparative transcriptomic analysis provides insights into the cold-adaptation mechanisms of a psychrophilic yeast, Glaciozyma antarctica PI12

Glaciozyma antarctica PI12, a psychrophilic yeast from Antarctica, grows well at low temperatures. However, it is not clear how it responds and adapts to cold and freeze stresses. Hence, this project was set out to determine the cold-adaptation strategies and mechanisms of G. antarctica PI12 using a transcriptomic analysis approach. G. antarctica PI12 cells, grown in rich medium at 12 °C, were exposed to freeze stress at 0 and − 12 °C for 6 h and 24 h. Their transcriptomes were sequenced and analyzed. A hundred and sixty-eight genes were differentially expressed. The yeast gene expression patterns were found to be dependent on the severity of the cold with more genes being differentially expressed at − 12 °C than at 0 °C. Glaciozyma antarctica PI12 was found to share some common adaptation strategies with other yeasts, Saccharomyces cerevisiae and Mrakia spp., but at the same time, found to have some of its own unique strategies and mechanisms. Among the unique mechanisms was the production of antifreeze protein to prevent ice-crystallization inside and outside the cell. In addition, several molecular chaperones, detoxifiers of reactive oxygen species (ROS), and transcription and translation genes were constitutively expressed in G. antarctica PI12 to enable the cells to endure the fluctuating freezing temperatures. Interestingly, G. antarctica PI12 used nitrite as an alternative terminal acceptor of electrons when the oxygen level was low to minimize disruption of energy production in the cell. These mechanisms coupled with several other common mechanisms ensured that G. antarctica PI12 adapted well to the cold temperatures

Identifcation of reference genes in chicken intraepithelial lymphocyte natural killer cells infected with very-virulent infectious bursal disease virus

Due to the limitations in the range of antibodies recognising avian viruses, quantitative real-time PCR (RT-qPCR) is still the most widely used method to evaluate the expression of immunologically related genes in avian viruses. The objective of this study was to identify suitable reference genes for mRNA expression analysis in chicken intraepithelial lymphocyte natural killer (IEL-NK) cells after infection with very-virulent infectious bursal disease virus (vvIBDV). Fifteen potential reference genes were selected based on the references available. The coefcient of variation percentage (CV%) and average count of these 15 genes were determined by NanoString technology for control and infected samples. The M and V values for shortlisted reference genes (ACTB, GAPDH, HMBS, HPRT1, SDHA, TUBB1 and YWHAZ) were calculated using geNorm and NormFinder. GAPDH, YWHAZ and HMBS were the most stably expressed genes. The expression levels of three innate immune response related target genes, CASP8, IL22 and TLR3, agreed in the NanoString and RNA sequencing (RNA-Seq) results using one or two reference genes for normalisation (not HMBS). In conclusion, GAPDH and YWHAZ could be used as reference genes for the normalisation of chicken IEL-NK cell gene responses to infection with vvIBDV

Mining multi-modal crime patterns at different levels of granularity using hierarchical clustering

The appearance of patterns could be found in different modalities of a domain, where the different modalities refer to the data sources that constitute different aspects of a domain. Particularly, the domain of our discussion refers to crime and the different modalities refer to the different data sources such as offender data, weapon data, etc. in crime domain. In addition, patterns also exist in different levels of granularity for each modality. In order to have a thorough understanding a domain, it is important to reveal the hidden patterns through the data explorations at different levels of granularity and for each modality. Therefore, this paper presents a new model for identifying patterns that exist in different levels of granularity for different modes of crime data. A hierarchical clustering approach - growing self organising maps (GSOM) has been deployed. Furthermore, the model is enhanced with experiments that exhibit the significance of exploring data at different granularities.<br /

Identifying multi-view patterns with hierarchy and granularity based multimodal (HGM) cognitive model

Humans perceive entities such as objects, patterns, events, etc. as concepts, which are the basic units in human intelligence and communications. In addition, perceptions of these entities could be abstracted and generalised at multiple levels of granularity. In particular, such granulation allows the formation and usage of concepts in human intelligence. Such natural granularity in human intelligence could inspire and motivate the design and development of pattern identification approach in Data Mining. In our opinion, a pattern could be perceived at multiple levels of granularity and thus we advocate for the co-existence of hierarchy and granularity. In addition, granular patterns exist across different sources of data (multimodality). In this paper, we present a cognitive model that incorporates the characteristics of Hierarchy, Granularity and Multimodality for multi-view patterns identification in crime domain. Such framework is implemented with Growing Self Organising Maps (GSOM) and some experimental results are presented and discussed.<br /

A brain inspired approach for multi-view patterns identification

Biologically human brain processes information in both uniimodal and multimodal approaches. In fact, information is progressively abstracted and seamlessly fused. Subsequently, the fusion of multimodal inputs allows a holistic understanding of a problem. The proliferation of technology has exponentially produced various sources of data, which could be likened to being the state of multimodality in human brain. Therefore, this is an inspiration to develop a methodology for exploring multimodal data and further identifying multi-view patterns. Specifically, we propose a brain inspired conceptual model that allows exploration and identification of patterns at different levels of granularity, different types of hierarchies and different types of modalities. A structurally adaptive neural network is deployed to implement the proposed model. Furthermore, the acquisition of multi-view patterns with the proposed model isdemonstrated and discussed with some experimental results.<br /

Application of a brain inspired model for profiling multi-view crime patterns

With the massive amount of crime data generated daily, this has put law enforcement under intensive stress. This means that law enforcement has to compete against the time to solve crime. In addition, the focus of crime investigation has been expanded from the ability to catch the criminals towards the ability to act before a crime happens (i.e pre-crime). Given such situation, creation of crime profiles is very important to law enforcement, especially in understanding the behaviours of criminals and identifying the characteristics of similar crimes. In fact, crime profiles could be used to solve similar crimes and thus pre-crime action could be conducted. In this paper, a brain inspired conceptual model is proposed and a structurally adaptive neural network is deployed for its implementation. Subsequently, the proposed model is applied for the identification and presentation of multi-view crime patterns. Such multi-view crime patterns could be useful for the construction of crime profiles. Moreover, the suitability of the proposed model in crime profiling is discussed and demonstrated through some experimental results.<br /