Proceedings of the Australasian Computer Science Week Multiconference最新文献_第5页

Naive Mesh-to-Mesh Coloured Model Generation using 3D GANs 幼稚的网格到网格的彩色模型生成使用3D gan

Proceedings of the Australasian Computer Science Week Multiconference Pub Date : 2020-01-29 DOI: 10.1145/3373017.3373067

Ryan J. Spick, Simon Demediuk, James Alfred Walker

{"title":"Naive Mesh-to-Mesh Coloured Model Generation using 3D GANs","authors":"Ryan J. Spick, Simon Demediuk, James Alfred Walker","doi":"10.1145/3373017.3373067","DOIUrl":"https://doi.org/10.1145/3373017.3373067","url":null,"abstract":"3D model creation forms a large part of the development process in 3D graphical environments such as games or simulations. If an unsupervised approach can be used to generate high-quality textured models the turnaround in these areas could be greatly improved. Advances in generative deep learning have been shown to understand even complex 3D structures, allowing neural networks to output generations learned from abundant model data. But there are no methods that incorporate colour channels into these techniques, an important factor when attempting to use the generations in an immersive environment. Proposed in this paper is an advancement on the initial voxel-based 3D generative adversarial network (GAN) learning to include colour within the output generated samples through adapting the channels of voxel inputs. Followed by the application of marching cubes to translate the voxel-based models into a naive coloured mesh. The method uses unsupervised learning but requires a target 3D textured model data set. The techniques shown in this paper were tested on a sparse collection of model inputs from a set of open access textured models. The method was tested on a data set of 24 variant models of fish. The outputs from the trained generative model in this paper show promising results, learning the shape and a variety of unique texture patterns.","PeriodicalId":297760,"journal":{"name":"Proceedings of the Australasian Computer Science Week Multiconference","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128829260","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 8

Partial Undersampling of Imbalanced Data for Cyber Threats Detection 网络威胁检测中不平衡数据的部分欠采样

Proceedings of the Australasian Computer Science Week Multiconference Pub Date : 2020-01-29 DOI: 10.1145/3373017.3373026

Mohd. Moniruzzaman, A. Bagirov, I. Gondal

引用次数: 4

Targeted Health Information Delivery Framework 目标卫生信息提供框架

Proceedings of the Australasian Computer Science Week Multiconference Pub Date : 2020-01-29 DOI: 10.1145/3373017.3373045

Heidi Bjering, J. A. Ginige

引用次数: 0

Effective Patient Similarity Computation for Clinical Decision Support Using Time Series and Static Data 基于时间序列和静态数据的临床决策支持的有效患者相似度计算

Proceedings of the Australasian Computer Science Week Multiconference Pub Date : 2020-01-29 DOI: 10.1145/3373017.3373050

M. Masud, Kadhim Hayawi, S. Mathew, A. Dirir, Muhsin Cheratta

{"title":"Effective Patient Similarity Computation for Clinical Decision Support Using Time Series and Static Data","authors":"M. Masud, Kadhim Hayawi, S. Mathew, A. Dirir, Muhsin Cheratta","doi":"10.1145/3373017.3373050","DOIUrl":"https://doi.org/10.1145/3373017.3373050","url":null,"abstract":"This paper presents a technique for computing patient similarity using time series data effectively combined with static data. Time series data of inpatients, such as heart rate, blood pressure, Oxygen saturation, respiration are measured at regular intervals, especially for inpatients in intensive care unit (ICU). The static data are mainly patient background and demographic data, including age, weight, height and gender. The similarity computation is done in unsupervised way. It is therefore free from data labeling requirement. However, such patient similarity can be very useful in developing various clinical decision support systems including treatment, medication, hospital admission and diagnosis. Our proposed technique works in three main steps. First, patient similarity is computed for each individual time series. Second, patients are grouped by clustering the static data. Finally, similarities from individual time series are combined and effectively blended with the patient group information to create a nearest neighborhood model. This model consists of a collection of the nearest neighbors for a given patient. We encounter several challenges for this task, including dealing with multi-variate time series data, variable sampling quantities and rates, missing values, and combining time-series with static data. We evaluate the proposed technique on a real patient database on two target features, namely, ‘diagnosis’ and ‘admission type’. Notable performance is recorded for both targets, achieving f1-score as high as 0.8. We believe this technique can effectively combine different types of clinical data and develop an efficient unsupervised framework for computing patient similarity to be utilized for clinical decision support systems.","PeriodicalId":297760,"journal":{"name":"Proceedings of the Australasian Computer Science Week Multiconference","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130158159","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

A Short Survey of Pre-trained Language Models for Conversational AI-A New Age in NLP 会话人工智能的预训练语言模型综述——NLP的新时代

Proceedings of the Australasian Computer Science Week Multiconference Pub Date : 2020-01-29 DOI: 10.1145/3373017.3373028

Munazza Zaib, Quan Z. Sheng, W. Zhang

{"title":"A Short Survey of Pre-trained Language Models for Conversational AI-A New Age in NLP","authors":"Munazza Zaib, Quan Z. Sheng, W. Zhang","doi":"10.1145/3373017.3373028","DOIUrl":"https://doi.org/10.1145/3373017.3373028","url":null,"abstract":"Building a dialogue system that can communicate naturally with humans is a challenging yet interesting problem of agent-based computing. The rapid growth in this area is usually hindered by the long-standing problem of data scarcity as these systems are expected to learn syntax, grammar, decision making, and reasoning from insufficient amounts of task-specific dataset. The recently introduced pre-trained language models have the potential to address the issue of data scarcity and bring considerable advantages by generating contextualized word embeddings. These models are considered counterpart of ImageNet in NLP and have demonstrated to capture different facets of language such as hierarchical relations, long-term dependency, and sentiment. In this short survey paper, we discuss the recent progress made in the field of pre-trained language models. We also deliberate that how the strengths of these language models can be leveraged in designing more engaging and more eloquent conversational agents. This paper, therefore, intends to establish whether these pre-trained models can overcome the challenges pertinent to dialogue systems, and how their architecture could be exploited in order to overcome these challenges. Open challenges in the field of dialogue systems have also been deliberated.","PeriodicalId":297760,"journal":{"name":"Proceedings of the Australasian Computer Science Week Multiconference","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116565264","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 50

Dynamically Recommending Repositories for Health Data: a Machine Learning Model 动态推荐健康数据存储库:一个机器学习模型

Proceedings of the Australasian Computer Science Week Multiconference Pub Date : 2020-01-29 DOI: 10.1145/3373017.3373041

M. Uddin, A. Stranieri, I. Gondal, V. Balasubramanian

{"title":"Dynamically Recommending Repositories for Health Data: a Machine Learning Model","authors":"M. Uddin, A. Stranieri, I. Gondal, V. Balasubramanian","doi":"10.1145/3373017.3373041","DOIUrl":"https://doi.org/10.1145/3373017.3373041","url":null,"abstract":"Recently, a wide range of digital health record repositories has emerged. These include Electronic Health record managed by the government, Electronic Medical Record (EMR) managed by healthcare providers, Personal Health Record (PHR) managed directly by the patient and new Blockchain-based systems mainly managed by technologies. Health record repositories differ from one another on the level of security, privacy, and quality of services (QoS) they provide. Health data stored in these repositories also varies from patient to patient in sensitivity, and significance depending on medical, personal preference, and other factors. Decisions regarding which digital record repository is most appropriate for the storage of each data item at every point in time are complex and nuanced. The challenges are exacerbated with health data continuously streamed from wearable sensors. In this paper, we propose a recommendation model for health data storage that can accommodate patient preferences and make storage decisions rapidly, in real-time, even with streamed data. The model maps health data to be stored in the repositories. The mapping between health data features and characteristics of each repository is learned using a machine learning-based classifier mediated through clinical rules. Evaluation results demonstrate the model’s feasibility.","PeriodicalId":297760,"journal":{"name":"Proceedings of the Australasian Computer Science Week Multiconference","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134443588","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 11

“I need to compartmentalize myself”: Appropriation of Instagram for chronic illness management “我需要把自己划分开来”:把Instagram用于慢性病管理

Proceedings of the Australasian Computer Science Week Multiconference Pub Date : 2020-01-29 DOI: 10.1145/3373017.3373040

N. Isika, Antonette Mendoza, R. Bosua

{"title":"“I need to compartmentalize myself”: Appropriation of Instagram for chronic illness management","authors":"N. Isika, Antonette Mendoza, R. Bosua","doi":"10.1145/3373017.3373040","DOIUrl":"https://doi.org/10.1145/3373017.3373040","url":null,"abstract":"Recent studies have suggested and explored the rapid increase of social media appropriation globally by users in across different usage contexts. In line with this phenomenon, there has been a surge in the number of chronically ill adults who leverage social media tools as part of their illness management practice. This paper applies an interpretive case study with a mixed method approach to investigate the processes and influences on appropriation of Instagram social media tool by chronically ill adults living with fibromyalgia. Our results highlight the processes of Instagram appropriation which include: Creating a separate account to compartmentalize fibromyalgia from “normal life”; borderless appropriation of multiple social media tools and; adaptive usage patterns to convey support on Instagram. Among a range of influences, information support exchanges to better manage living with fibromyalgia; emotional support exchanges and validation; monetary/financial benefits: reaching a wider, targeted audience; accessibility and ease of use of Instagram were found to be positively influence appropriation of Instagram by chronically ill adults.","PeriodicalId":297760,"journal":{"name":"Proceedings of the Australasian Computer Science Week Multiconference","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124892338","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 7

Motivational Factors of Australian Mobile Gamers 分析澳大利亚手机玩家的动机因素

Proceedings of the Australasian Computer Science Week Multiconference Pub Date : 2020-01-29 DOI: 10.1145/3373017.3373066

Jordan Greenwood, Leigh Achterbosch, Grant Meredith, P. Vamplew

{"title":"Motivational Factors of Australian Mobile Gamers","authors":"Jordan Greenwood, Leigh Achterbosch, Grant Meredith, P. Vamplew","doi":"10.1145/3373017.3373066","DOIUrl":"https://doi.org/10.1145/3373017.3373066","url":null,"abstract":"Mobile games are a fast growing industry, overtaking all other video game platforms with year on year increases in revenue. Many studies have been conducted to explore the motivations of why video games players play their selected games. However very little research has focused on mobile gamers. In addition, Australian studies on the topic are sparse. This paper aimed to discover what motivates a mobile gamer from the perspective of the initial motivational factors attracting them to a mobile game, and the motivational factors that provide interest to continue playing and thereby increase game longevity. A survey was conducted online for Australian participants, which attracted 123 respondents. The survey was formulated by focusing on the 12 key subcomponents as motivational factors of the Gamer Motivational Profile v2 model devised by Quantic Foundry. It was discovered that mobile gamers are a completely different breed of gamer in contrast to the general video gamer. Strategy and challenge which are subcomponents of mastery proved popular among all mobile gamers, while destruction and excitement, subcomponents of action, were often the least motivating factors of all. With the newly discovered data, perhaps mobile game developers can pursue the correct avenues of game design when catering to their target audience.","PeriodicalId":297760,"journal":{"name":"Proceedings of the Australasian Computer Science Week Multiconference","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130098699","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

HHH: An Online Medical Chatbot System based on Knowledge Graph and Hierarchical Bi-Directional Attention 基于知识图和分层双向关注的在线医疗聊天机器人系统

Proceedings of the Australasian Computer Science Week Multiconference Pub Date : 2020-01-29 DOI: 10.1145/3373017.3373049

Qiming Bao, Lin Ni, J. Liu

引用次数: 34

Understanding the Comorbidity of Multiple Chronic Diseases Using a Network Approach 用网络方法了解多种慢性疾病的合并症

Proceedings of the Australasian Computer Science Week Multiconference Pub Date : 2019-01-29 DOI: 10.1145/3290688.3290730

Md Ekramul Hossain, Arif Khan, M. S. Uddin

{"title":"Understanding the Comorbidity of Multiple Chronic Diseases Using a Network Approach","authors":"Md Ekramul Hossain, Arif Khan, M. S. Uddin","doi":"10.1145/3290688.3290730","DOIUrl":"https://doi.org/10.1145/3290688.3290730","url":null,"abstract":"Chronic diseases and associated conditions are the leading causes of death in most of the countries worldwide. Due to this, governments all over the world are concerned about the burden of chronic diseases. These diseases often pose severe health risks to the patients when they suffer from more than one chronic disease at the same time (also named as comorbidity of chronic disease). Several prediction approaches utilizing routinely collected administrative healthcare data have been developed for the prevention and better management of comorbidity. Most studies to date have focused on understanding the progression of single chronic disease rather than multiple chronic diseases. In this study, a research framework is proposed using social network analysis and graph theory using administrative healthcare data to understand the comorbidity of two chronic diseases (i.e., type 2 diabetes (T2D) leading to the development of cardiovascular disease). The results show that diseases related to blood (e.g., high blood pressure or high cholesterol) and kidney disease occurred frequently during the progression of cardiovascular disease for the T2D patients. The proposed framework could be useful for stakeholders including governments and health insurers to adopt appropriate prevention or management program for the patients at high risk of developing multiple chronic diseases.","PeriodicalId":297760,"journal":{"name":"Proceedings of the Australasian Computer Science Week Multiconference","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121139084","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6