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Learning To Dance, Skye Playsted Jan 2020

Learning To Dance, Skye Playsted

Faculty of Social Sciences - Papers (Archive)

No abstract provided.


From Ideal To Reality: Segmentation, Annotation, And Recommendation, The Vital Trajectory Of Intelligent Micro Learning, Jiayin Lin, Geng Sun, Tingru Cui, Jun Shen, Dongming Xu, Ghassan Beydoun, Ping Yu, David Pritchard, Li Li, Shiping Chen Jan 2020

From Ideal To Reality: Segmentation, Annotation, And Recommendation, The Vital Trajectory Of Intelligent Micro Learning, Jiayin Lin, Geng Sun, Tingru Cui, Jun Shen, Dongming Xu, Ghassan Beydoun, Ping Yu, David Pritchard, Li Li, Shiping Chen

Faculty of Engineering and Information Sciences - Papers: Part B

The soaring development of Web technologies and mobile devices has blurred time-space boundaries of people’s daily activities. Such development together with the life-long learning requirement give birth to a new learning style, micro learning. Micro learning aims to effectively utilize learners’ fragmented time to carry out personalized learning activities through online education resources. The whole workflow of a micro learning system can be separated into three processing stages: micro learning material generation, learning materials annotation and personalized learning materials delivery. Our micro learning framework is firstly introduced in this paper from a higher perspective. Then we will review representative segmentation …


Evolutionary Learner Profile Optimization Using Rare And Negative Association Rules For Micro Open Learning, Geng Sun, Jiayin Lin, Jun Shen, Tingru Cui, Dongming Xu, Huaming Chen Jan 2020

Evolutionary Learner Profile Optimization Using Rare And Negative Association Rules For Micro Open Learning, Geng Sun, Jiayin Lin, Jun Shen, Tingru Cui, Dongming Xu, Huaming Chen

Faculty of Engineering and Information Sciences - Papers: Part B

The actual data availability, readiness and publicity has slowed down the research of making use of computational intelligence to improve the knowledge delivery in an emerging learning mode, namely adaptive micro open learning, which naturally has high demand in quality and quantity of data to be fed. In this study, we contribute a novel approach to tackle the current scarcity of both data and rules in micro open learning, by adopting evolutionary algorithm to produce association rules with both rare and negative associations taken into account. These rules further drive the generation and optimization of learner profiles through refinement and …


5 Tips To Help Parents Navigate The Unique Needs Of Children With Autism Learning From Home, Amanda A. Webster Jan 2020

5 Tips To Help Parents Navigate The Unique Needs Of Children With Autism Learning From Home, Amanda A. Webster

Faculty of Arts, Social Sciences and Humanities - Papers

Children on the autism spectrum will face unique challenges as they learn from home during the COVID-19 shutdown. These children process information and learn in different ways to their peers. They may find it more difficult to independently complete tasks and struggle with managing their time. They are also more likely to have difficulty in writing tasks or ones involving high amounts of language and communication with others. Children on the autism spectrum often associate specific tasks with locations. This might mean they resist doing schoolwork at home. Anxiety levels, which are often high in this group of students, may …


Apex2s: A Two-Layer Machine Learning Model For Discovery Of Host-Pathogen Protein-Protein Interactions On Cloud-Based Multiomics Data, Huaming Chen, Jun Shen, Lei Wang, Chi-Hung Chi Jan 2020

Apex2s: A Two-Layer Machine Learning Model For Discovery Of Host-Pathogen Protein-Protein Interactions On Cloud-Based Multiomics Data, Huaming Chen, Jun Shen, Lei Wang, Chi-Hung Chi

Faculty of Engineering and Information Sciences - Papers: Part B

No abstract provided.


Deep Sequence Labelling Model For Information Extraction In Micro Learning Service, Jiayin Lin, Zhexuan Zhou, Geng Sun, Jun Shen, David Pritchard, Tingru Cui, Dongming Xu, Li Li, Ghassan Beydoun Jan 2020

Deep Sequence Labelling Model For Information Extraction In Micro Learning Service, Jiayin Lin, Zhexuan Zhou, Geng Sun, Jun Shen, David Pritchard, Tingru Cui, Dongming Xu, Li Li, Ghassan Beydoun

Faculty of Engineering and Information Sciences - Papers: Part B

Micro learning aims to assist users in making good use of smaller chunks of spare time and provides an effective online learning service. However, to provide such personalized online services on the Web, a number of information overload challenges persist. Effectively and precisely mining and extracting valuable information from massive and redundant information is a significant preprocessing procedure for personalizing online services. In this study, we propose a deep sequence labelling model for locating, extracting, and classifying key information for micro learning services. The proposed model is general and combines the advantages of different types of classical neural network. Early …


Refinement And Augmentation For Data In Micro Learning Activity With An Evolutionary Rule Generators, Geng Sun, Jiayin Lin, Tingru Cui, Jun Shen, Dongming Xu, Mahesh Kayastha Jan 2020

Refinement And Augmentation For Data In Micro Learning Activity With An Evolutionary Rule Generators, Geng Sun, Jiayin Lin, Tingru Cui, Jun Shen, Dongming Xu, Mahesh Kayastha

Faculty of Engineering and Information Sciences - Papers: Part B

Improving both the quantity and quality of existing data are placed at the center of research for adaptive micro open learning. To cover this research gap, our work targets on the current scarcity of both data and rules that represent open learning activities. An evolutionary rule generator is constructed, which consists of an outer loop and an inner loop. The outer loop runs a genetic algorithm (GA) to produce association rules that can be effective in the micro open learning scenario from a small amount of available data sources; while the inner loop optimizes generated candidates by taking into account …


Ensemble Machine Learning Approaches For Webshell Detection In Internet Of Things Environments, Binbin Yong, Wei Wei, Kuan-Ching Li, Jun Shen, Qingguo Zhou, Marcin Wozniak, Dawid Polap, Robertas Damasevicius Jan 2020

Ensemble Machine Learning Approaches For Webshell Detection In Internet Of Things Environments, Binbin Yong, Wei Wei, Kuan-Ching Li, Jun Shen, Qingguo Zhou, Marcin Wozniak, Dawid Polap, Robertas Damasevicius

Faculty of Engineering and Information Sciences - Papers: Part B

The Internet of things (IoT), made up of a massive number of sensor devices interconnected, can be used for data exchange, intelligent identification, and management of interconnected “things.” IoT devices are proliferating and playing a crucial role in improving the living quality and living standard of the people. However, the real IoT is more vulnerable to attack by countless cyberattacks from the Internet, which may cause privacy data leakage, data tampering and also cause significant harm to society and individuals. Network security is essential in the IoT system, and Web injection is one of the most severe security problems, especially …


Deep Autoencoder For Mass Spectrometry Feature Learning And Cancer Detection, Qingguo Zhou, Binbin Yong, Qingquan Lv, Jun Shen, Xin Wang Jan 2020

Deep Autoencoder For Mass Spectrometry Feature Learning And Cancer Detection, Qingguo Zhou, Binbin Yong, Qingquan Lv, Jun Shen, Xin Wang

Faculty of Engineering and Information Sciences - Papers: Part B

Cancer is still one of the most life threatening disease and by far it is still difficult to prevent, prone to recurrence and metastasis and high in mortality. Lots of studies indicate that early cancer diagnosis can effectively increase the survival rate of patients. But early stage cancer is difficult to be detected because of its inconspicuous features. Hence, convenient and effective cancer detection methods are urgently needed. In this paper, we propose to utilize deep autoencoder to learn latent representation of high-dimensional mass spectrometry data. Meanwhile, as a contrast, traditional particle swarm optimization (PSO) optimization algorithm are also used …


Using Cost-Sensitive Learning And Feature Selection Algorithms To Improve The Performance Of Imbalanced Classification, Fang Feng, Kuan-Ching Li, Jun Shen, Qingguo Zhou, Xuhui Yang Jan 2020

Using Cost-Sensitive Learning And Feature Selection Algorithms To Improve The Performance Of Imbalanced Classification, Fang Feng, Kuan-Ching Li, Jun Shen, Qingguo Zhou, Xuhui Yang

Faculty of Engineering and Information Sciences - Papers: Part A

Imbalanced data problem is widely present in network intrusion detection, spam filtering, biomedical engineering, finance, science, being a challenge in many real-life data-intensive applications. Classifier bias occurs when traditional classification algorithms are used to deal with imbalanced data. As already known, the General Vector Machine (GVM) algorithm has good generalization ability, though it does not work well for the imbalanced classification. Additionally, the state-of-the-art Binary Ant Lion Optimizer (BALO) algorithm has high exploitability and fast convergence rate. Based on these facts, we have proposed in this paper a Cost-sensitive Feature selection General Vector Machine (CFGVM) algorithm based on GVM and …


Automatic Ventricular Nuclear Magnetic Resonance Image Processing With Deep Learning, Binbin Yong, Chen Wang, Jun Shen, Fucun Li, Hang Yin Jan 2020

Automatic Ventricular Nuclear Magnetic Resonance Image Processing With Deep Learning, Binbin Yong, Chen Wang, Jun Shen, Fucun Li, Hang Yin

Faculty of Engineering and Information Sciences - Papers: Part A

Cardiovascular diseases (CVD) seriously threaten the health of human beings, and they have caused widespread concern in recent years. At present, the diagnosis of CVD is mainly conducted by computed tomography (CT), echocardiography and nuclear magnetic resonance (NMR) technologies. NMR imaging technology is widely used in medical applications owing to its characteristics of high resolution and very low radiation. However, manual NMR image segmentation is time-consuming and error-prone, which has led to the research on automatic NMR image segmentation technologies. Researchers tend to explore the ventricular NRM image segmentation to improve the accuracy of CVD diagnosis. In this study, based …


Hybrid Translation And Language Model For Micro Learning Material Recommendation, Jiayin Lin Jan 2020

Hybrid Translation And Language Model For Micro Learning Material Recommendation, Jiayin Lin

Faculty of Engineering and Information Sciences - Papers: Part A

As an emerging pedagogy, micro learning aims to make use of people’s fragmented spare time and provide personalized online learning service, for example, by pushing fragmented knowledge to specific learners. In the context of big data, the recommender system is the key factor for realizing the online personalization service, which significantly determines what information will be fmally accessed by the target learners. In the education discipline, due to the pedagogical requirements and the domain characteristics, ranking recommended learning materials is essential for maintaining the outcome of the massive learning scenario. However, many widely used recommendation strategies in other domains showed …


"A Big Influence On My Teaching Career And My Life": A Longitudinal Study Of Learning To Teach English Pronunciation, Michael S. Burri, Amanda Ann Baker Jan 2020

"A Big Influence On My Teaching Career And My Life": A Longitudinal Study Of Learning To Teach English Pronunciation, Michael S. Burri, Amanda Ann Baker

Faculty of Social Sciences - Papers (Archive)

Inquiry into learning to teach pronunciation is a growing area within the second language teacher education research paradigm. To what extent this learning process extends into instructors' early years of teaching pronunciation has yet to be explored. This article is a response to this need by exploring the 3.5-year trajectory of five teachers learning to teach English pronunciation. The study was conducted in two phases. In Phase 1, pre- and post-course questionnaires, weekly observations of the lectures, focus groups interviews, final post-course interviews, and the participants' final assessment task were triangulated to examine the development of participants' cognitions during a …


How Students Learn On Placement: Transitioning Placement Practices In Work-Integrated Learning, Bonnie Amelia Dean, Christopher S. Sykes Jan 2020

How Students Learn On Placement: Transitioning Placement Practices In Work-Integrated Learning, Bonnie Amelia Dean, Christopher S. Sykes

Faculty of Science, Medicine and Health - Papers: Part B

Examining learning in work-integrated learning (WIL) courses is complex. WIL traverses work and university spaces, which can be challenging for the way student learning is conceived, planned, supported, assessed and reported. This study strengthens our understanding of how students learn on placement by going directly to the source and observing learning unfold, in situ. Using an ethnographic methodology, this study adopts Schatzki’s (1996, 2010) practice-based lens to illuminate how students learn to embody and accomplish their assigned tasks on WIL placement. Findings suggest that students initially learn through performing an intermediary cluster of practices that enable them to orient, adapt …


Evaluating Techniques For Mapping Island Vegetation From Unmanned Aerial Vehicle (Uav) Images: Pixel Classification, Visual Interpretation And Machine Learning Approaches, Sarah Hamylton, Rowena H. Morris, Rafael Cabral Carvalho, N Roder, P Barlow, K Mills, Lei Wang Jan 2020

Evaluating Techniques For Mapping Island Vegetation From Unmanned Aerial Vehicle (Uav) Images: Pixel Classification, Visual Interpretation And Machine Learning Approaches, Sarah Hamylton, Rowena H. Morris, Rafael Cabral Carvalho, N Roder, P Barlow, K Mills, Lei Wang

Faculty of Science, Medicine and Health - Papers: Part B

We evaluate three approaches to mapping vegetation using images collected by an unmanned aerial vehicle (UAV) to monitor rehabilitation activities in the Five Islands Nature Reserve, Wollongong (Australia). Between April 2017 and July 2018, four aerial surveys of Big Island were undertaken to map changes to island vegetation following helicopter herbicide sprays to eradicate weeds, including the creeper Coastal Morning Glory (Ipomoea cairica) and Kikuyu Grass (Cenchrus clandestinus). The spraying was followed by a large scale planting campaign to introduce native plants, such as tussocks of Spiny-headed Mat-rush (Lomandra longifolia). Three approaches to mapping vegetation were evaluated, including: (i) a …


Leadership For Learning Frameworks, Kylie Lipscombe, Sue Bennett, Paul Andrew Kidson, Paul Gardiner, Ann Mcintyre Jan 2020

Leadership For Learning Frameworks, Kylie Lipscombe, Sue Bennett, Paul Andrew Kidson, Paul Gardiner, Ann Mcintyre

Faculty of Arts, Social Sciences and Humanities - Papers

The purpose of this report is to present the findings of a commissioned research study by the University of Wollongong (UOW) for the NSW DoE School Leadership Institute (SLI). The study is focused on examining the conceptualisation and design of three Leadership for Learning Frameworks implemented as part of the Aspiring Principals Leadership Program (APLP).


Conducting Qualitative Longitudinal Research On Learning To Teach English Pronunciation: Challenges, Pitfalls…Coffee, And Bubbles!, Michael S. Burri Jan 2020

Conducting Qualitative Longitudinal Research On Learning To Teach English Pronunciation: Challenges, Pitfalls…Coffee, And Bubbles!, Michael S. Burri

Faculty of Arts, Social Sciences and Humanities - Papers

Inquiries into the impact of second language teacher education on the development of teachers' practices, beliefs, and knowledge have increased substantially in the last few years. However, most studies tend to investigate the process of second language teacher learning over a relatively short period of time, and only limited literature addresses methodological considerations in longitudinal research, making the design of this type of study potentially challenging for researchers. The aim of this paper is to first describe an ongoing project which explores the process of teachers learning to teach English pronunciation over a period of six years. Following an overview …