Proceedings of the 2021 Australasian Computer Science Week Multiconference最新文献_第3页

A Comparative Study of ML-ELM and DNN for Intrusion Detection ML-ELM与DNN在入侵检测中的比较研究

Proceedings of the 2021 Australasian Computer Science Week Multiconference Pub Date : 2021-02-01 DOI: 10.1145/3437378.3437390

Wencheng Yang, Song Wang, Michael N. Johnstone

引用次数: 3

Melanoma classification using EfficientNets and Ensemble of models with different input resolution 使用不同输入分辨率的effentnets和集成模型进行黑色素瘤分类

Proceedings of the 2021 Australasian Computer Science Week Multiconference Pub Date : 2021-02-01 DOI: 10.1145/3437378.3437396

Sagar Karki, Pradnya Kulkarni, A. Stranieri

引用次数: 11

Archetypal Analysis Based Anomaly Detection for Improved Storytelling in Multiplayer Online Battle Arena Games 基于原型分析的异常检测在多人在线竞技游戏中改善故事叙述

Proceedings of the 2021 Australasian Computer Science Week Multiconference Pub Date : 2021-02-01 DOI: 10.1145/3437378.3442690

R. Sifa, Anders Drachen, Florian Block, Spencer Moon, Anisha Dubhashi, Hao Xiao, Zili Li, D. Klabjan, Simon Demediuk

{"title":"Archetypal Analysis Based Anomaly Detection for Improved Storytelling in Multiplayer Online Battle Arena Games","authors":"R. Sifa, Anders Drachen, Florian Block, Spencer Moon, Anisha Dubhashi, Hao Xiao, Zili Li, D. Klabjan, Simon Demediuk","doi":"10.1145/3437378.3442690","DOIUrl":"https://doi.org/10.1145/3437378.3442690","url":null,"abstract":"Anomalies in esports refer to situations when something unexpected or unlikely happens. Rapid performance changes, unusual strategies, extraordinary plays, accelerated resource gains or team wipeouts comprise examples, but anomalies fundamentally comprise any situation where something unexpected happens. In multi-player online esports games such as multi-player online battle arena games, anomalies form a key component of the commentator-driven storytelling. In fast-paced, complex esports titles, anomalies can however go undetected until it is too late for commentators to note them and use them in their coverage, and for viewers they can take place outside the viewable area of the broadcast stream. Furthermore, there are limited tools available for commentators and players across professional and amateur levels for analysing or categorising anomalies. The research presented here provides a novel approach towards identifying one type of outliers in esports matches, via the application of archetype analysis to extract novel insights that can be used by commentators to improve esports coverage. As a case example, the major esports title League of Legends is used. We present a viable methodology for utilizing distributions resulting from the archetypal clusters and reconstruction errors to expose and explain anomalous events during gameplay.","PeriodicalId":146881,"journal":{"name":"Proceedings of the 2021 Australasian Computer Science Week Multiconference","volume":"385 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126730579","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: 5

Handling uncertainty using features from pathology: opportunities in primary care data for developing high risk cancer survival methods 利用病理学特征处理不确定性:在初级保健数据中开发高风险癌症生存方法的机会

Proceedings of the 2021 Australasian Computer Science Week Multiconference Pub Date : 2020-12-17 DOI: 10.1145/3437378.3437381

G. Ristanoski, J. Emery, J. Martinez-Gutierrez, D. McCarthy, U. Aickelin

{"title":"Handling uncertainty using features from pathology: opportunities in primary care data for developing high risk cancer survival methods","authors":"G. Ristanoski, J. Emery, J. Martinez-Gutierrez, D. McCarthy, U. Aickelin","doi":"10.1145/3437378.3437381","DOIUrl":"https://doi.org/10.1145/3437378.3437381","url":null,"abstract":"More than 144 000 Australians were diagnosed with cancer in 2019. Diagnosing cancer in primary care is challenging due to the non-specific nature of cancer symptoms and its low prevalence. Understanding the epidemiology of cancer symptoms and patterns of presentation in patient's medical history from primary care data could be important to improve earlier detection and cancer outcomes. As past medical data about a patient can be incomplete, irregular or missing, this creates additional challenges when attempting to use the patient's history for any new diagnosis. Our research aims to investigate the opportunities in a patient's pathology history available to a GP, initially focused on the results within the frequently ordered full blood count to determine relevance to a future high-risk cancer prognosis, and treatment outcome. We investigated how past pathology test results can lead to deriving features that can be used to predict cancer outcomes, with emphasis on patients at risk of not surviving the cancer within 2-year period. This initial work focuses on patients with lung cancer, although the methodology can be applied to other types of cancer and other data within the medical record. Our findings indicate that even in cases of incomplete or obscure patient history, hematological measures can be useful in generating features relevant for predicting cancer risk and survival. The results strongly indicate to add the use of pathology test data for potential high-risk cancer diagnosis, and utilize additional pathology metrics or other primary care datasets even more for similar purposes.","PeriodicalId":146881,"journal":{"name":"Proceedings of the 2021 Australasian Computer Science Week Multiconference","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122148647","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: 0

On the Importance of Diversity in Re-Sampling for Imbalanced Data and Rare Events in Mortality Risk Models 死亡率风险模型中不平衡数据和罕见事件重抽样多样性的重要性

Proceedings of the 2021 Australasian Computer Science Week Multiconference Pub Date : 2020-12-15 DOI: 10.1145/3437378.3437382

Yuxuan Yang, H. A. Khorshidi, U. Aickelin, Aditi Nevgi, E. Ekinci

{"title":"On the Importance of Diversity in Re-Sampling for Imbalanced Data and Rare Events in Mortality Risk Models","authors":"Yuxuan Yang, H. A. Khorshidi, U. Aickelin, Aditi Nevgi, E. Ekinci","doi":"10.1145/3437378.3437382","DOIUrl":"https://doi.org/10.1145/3437378.3437382","url":null,"abstract":"Surgical risk increases significantly when patients present with comorbid conditions. This has resulted in the creation of numerous risk stratification tools with the objective of formulating associated surgical risk to assist both surgeons and patients in decision-making. The Surgical Outcome Risk Tool (SORT) is one of the tools developed to predict mortality risk throughout the entire perioperative period for major elective in-patient surgeries in the UK. In this study, we enhance the original SORT prediction model (UK SORT) by addressing the class imbalance within the dataset. Our proposed method investigates the application of diversity-based selection on top of common re-sampling techniques to enhance the classifier's capability in detecting minority (‘mortality’) events. Diversity amongst training datasets is an essential factor in ensuring re-sampled data keeps an accurate depiction of the minority/majority class region, thereby solving the generalization problem of mainstream sampling approaches. We incorporate the use of the Solow-Polasky measure as a drop-in functionality to evaluate diversity, with the addition of greedy algorithms to identify and discard subsets that share the most similarity. Additionally, through empirical experiments, we prove that the performance of the classifier trained over diversity-based dataset outperforms the original classifier over ten external datasets. Our diversity-based re-sampling method elevates the performance of the UK SORT algorithm by 1.4%.","PeriodicalId":146881,"journal":{"name":"Proceedings of the 2021 Australasian Computer Science Week Multiconference","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129342298","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: 5