Yong-Bin Kang

Yong-Bin Kang

Senior data science research fellow for Centre of Excellence for Automated Decision Making and Society (ADM+S), Swinburne University of Technology

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Posts

Future Blog Post

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Blog Post number 4

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Blog Post number 1

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publications

Character Grouping Technique Using 3-D Neighborhood Graphs in Raster Map (1998)

Yong-Bin Kang, Se-Young Ok, Hwan-Gue Cho, Character Grouping Technique Using 3-D Neighborhood Graphs in Raster Map, 14th International Conference on Pattern Recognition (ICPR'98),

A New Route Determination Approach using Future Traffic Prediction (2004)

Yong-Bin Kang, Sung-Soo Kim, A New Route Determination Approach using Future Traffic Prediction, Journal of WSEAS Transactions on Systems, Vol. 4(6), pp. 804 - 811, 2005

A Survey on Projector-based PC Cluster Distributed Large Screen Displays and Shader Technologies (2007)

Munjae Song, Seongwon Park, and Yong-Bin Kang, A Survey on Projector-Based PC Cluster Distributed Large Screen Displays and Shader Technologies, The 2007 International Conference on Computer Graphics and Virtual Reality (CGVR '07), June 25-28 2007 (Acceptance Rate: 28%)

Congestion Avoidance Algorithm using Extended Kalman Filter (2007)

Sung-Soo Kim and Yong-Bin Kang, Congestion Avoidance Algorithm using Extended Kalman Filter, The 2007 International Conference on Convergence Information Technology (ICCIT 07), IEEE CS, Nov. 2007 (Acceptance Rate: 29%)

XMegaWall: A Super High-Resolution Tiled Display using a PC Cluster (2007)

Yong-Bin Kang, Ki-Joon Chae, XMegaWall: A Super High-Resolution Tiled Display using a PC Cluster, Proceeding of Computer Graphics International (CGI), 2007, pp. 29-36, May 30th - July 2nd 2007 (Acceptance Rate: 17%)

A scalable, high-resolution tiled display system (2007)

Yong-Bin Kang, A scalable, high-resolution tiled display system, International MultiConference of Engineers and Computer Scientists (IMECS 2007), pp. 2009-2013, 2007

A Personalization Approach for Problem Management (2008)

Yong-Bin Kang, Arkady Zaslavsky, Shonali Krishnaswamy, and Claudio Bartolini, A Personalization Approach for Problem Management", 15th HP Software University Association (HP-SUA) Workshop, 22-25 June, Marrakech, Morocco, 2008

Help-Desk Agent Recommendation System Based on Three-Layered User Profile (2008)

Yong-Bin Kang, Arkady Zaslavsky, and Shonali Krishnaswamy, Help-desk Agent Recommendation System Based on Three-layered User Profile,Ê European Conference on Artificial Intelligence (ECAI) 2008 Workshop on Recommender Systems, 21-22 July, 2008

A Computer-Facilitated Method for Matching Incident Cases Using Semantic Similarity Measurement (2009)

Yong-Bin Kang, Arkady Zaslavsky, Shonali Krishnaswamy, and Claudio Bartolini, A Computer-Facilitated Method for Matching Incident Cases Using Semantic Similarity Measurement", 4th IFIP/IEEE International Workshop on Business-driven IT Management (BDIM 2009), 2009 (Selected for In-depth discussion paper - only 3 papers were selected)

A Knowledge-rich Similarity Measure for Improving IT Incident Resolution Process (2010)

Yong-Bin Kang, Arkady Zaslavsky, Shonali Krishnaswamy, and Claudio Bartolini, A knowledge-rich similarity measure for improving IT incident resolution process, Proceedings of the 2010 ACM Symposium on Applied Computing, pp. 1781-1788 (Acceptance Rate: 22%)

Retrieval in CBR Using a Combination of Similarity and Association Knowledge (2011)

Yong-Bin Kang, Shonali Krishnaswamy, and Arkady Zaslavsky, Retrieval in CBR Using a Combination of Similarity and Association Knowledge, 7th International Conference on Advanced Data Mining and Applications (ADMA 2011) (CoRE rank B), pp. 1-14, December 2011 (Acceptance Rate: 21%)

A Case Retrieval Approach Using Similarity and Association Knowledge (2011)

Yong-Bin Kang, Shonali Krishnaswamy, and Arkady Zaslavsky, A Case Retrieval Approach Using Similarity and Association Knowledge, 19th International Conference on Cooperative Information Systems (CoopIS) (CoRE rank A), pp. 218-235, October 2011 (Acceptance Rate: 21%)

A Retrieval Strategy Using the Integrated Knowledge of Similarity and Associations (2011)

Yong-Bin Kang, Shonali Krishnaswamy, and Arkady Zaslavsky, A Retrieval Strategy using the Integrated Knowledge of Similarity and Associations, The 16th International Conference on Database Systems for Advanced Applications (DASFAA) (CoRE rank A), pp. 16-30, April 2011 (Acceptance Rate: 24%)

Predicting Reasoning Performance Using Ontology Metrics (2012)

ong -Bin Kang, Yuan-Fang Li, and Shonali Krishnaswamy, Predicting Reasoning Performance Using Ontology Metrics, 11th International Semantic Web Conference (ISWC 2012), pp 198-214, Nov 11-15, 2012 (Acceptance Rate: 22%)

A Meta-reasoner to Rule Them All (2014)

Yong-Bin Kang, Shonali Krishnaswamy, and Yuan-Fang Li, A Meta-reasoner to Rule Them All, 23rd International Conference on Information and Knowledge Management (CIKM) (CoRE rank A), pp 1935-1938, Shanghai, China, Nov 3-7, 2014

CFinder: An intelligent key concept finder from text for ontology development (2014)

Yong-Bin Kang, Pari Delir Haghighi, Frada Burstein, CFinder: An intelligent key concept finder from text for ontology development, Expert Systems with Applications, Volume 41, Issue 9, 2014, Pages 4494-4504, ISSN 0957-4174, https://doi.org/10.1016/j.eswa.2014.01.006.

How Long Will It Take? Accurate Prediction of Ontology Reasoning Performance (2014)

Yong -Bin Kang, Yuan-Fang Li, and Shonali Krishnaswamy, How Long Will It Take? Accurate Prediction of Ontology Reasoning Performance, Twenty-Eighth AAAI Conference on Artificial Intelligence (AAAI-14), pp 80 - 86, July 27 - 31, 2014 (Acceptance Rate: 28%)

A Retrieval Strategy for Case-Based Reasoning Using Similarity and Association Knowledge (2014)

Yong-Bin Kang, Shonali Krishnaswamy, and Arkady Zaslavsky, "A Retrieval Strategy for Case-Based Reasoning Using Similarity and Association Knowledge," in IEEE Transactions on Cybernetics, vol. 44, no. 4, pp. 473-487, April 2014, doi: 10.1109/TCYB.2013.2257746.

R2O2: An Efficient Ranking-Based Reasoner for OWL Ontologies (2015)

Yong-Bin Kang, Yuan-Fang Li, and Shonali Krishnaswamy, R2O2: an Efficient Ranking-based Reasoner for OWL Ontologies, 14th International Semantic Web Conference (ISWC), Bethlehem, Pennsylvania, USA, October 11-15, 2015

TaxoFinder: A Graph-Based Approach for Taxonomy Learning (2016)

Yong-Bin Kang, Pari Delir Haghighi, and Frada Burstein, TaxoFinder: A Graph-Based Approach for Taxonomy Learning, IEEE Transactions on Knowledge and Data Engineering, vol. 28, no. 2, pp. 524-536, 1 Feb. 2016, doi: 10.1109/TKDE.2015.2475759.

Investigating subjective experience and the influence of weather among individuals with fibromyalgia: a content analysis of Twitter (2017)

Pari Delir Haghighi, Yong-Bin Kang, Rachelle Buchbinder, Frada Burstein, Samuel Whittle, Investigating Subjective Experience and the Influence of Weather Among Individuals With Fibromyalgia: A Content Analysis of Twitter, JMIR public health and surveillance, Vol 3, No 1, 2017

A solution for annotating sensor data streams - An industrial use case in building management system (2020)

Dumindu Madithiyagasthenna, Prem Prakash Jayaraman, Ahsan Morshed, Abdur Rahim Mohammad Forkan, Dimitrios Georgakopoulos, Yong-Bin Kang, Mirek Piechowski, "A solution for annotating sensor data streams - An industrial use case in building management system," 2020 21st IEEE International Conference on Mobile Data Management (MDM), 2020, pp. 194-201, doi: 10.1109/MDM48529.2020.00042.

ECHO: A Tool for Empirical Evaluation Cloud Chatbots (2020)

Abdur Rahim Mohammad Forkan, Prem Prakash Jayaraman, Yong-Bin Kang, Ahsan Morshed, "ECHO: A Tool for Empirical Evaluation Cloud Chatbots," 2020 20th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing (CCGRID), 2020, pp. 669-672, doi: 10.1109/CCGrid49817.2020.00-26.

Towards Meta-reasoning for Ontologies: A Roadmap (2020)

Yuan-Fang Li, Yong-Bin Kang, Towards Meta-reasoning for Ontologies: A Roadmap, 24th European Conference on Artificial Intelligence (ECAI 2020)

Understanding and improving ontology reasoning efficiency through learning and ranking (2020)

Yong-Bin Kang, Shonali Krishnaswamy, Wudhichart Sawangphol, Lianli Gao, Yuan-Fang Li, Understanding and improving ontology reasoning efficiency through learning and ranking, Information Systems, Volume 87, 2020, 101412, ISSN 0306-4379, https://doi.org/10.1016/j.is.2019.07.002.

XL-Sum: Large-Scale Multilingual Abstractive Summarization for 44 Languages (2021)

Hasan, Tahmid; Bhattacharjee, Abhik; Islam, Md. Saiful; Samin, Kazi; Li, Yuan-Fang; Kang, Yong-Bin; Rahman, M. Sohel; Shahriyar, Rifat. XL-Sum: Large-Scale Multilingual Abstractive Summarization for 44 Languages. Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (ACL-IJCNLP'21)

This paper proposes a very large dataset for multilingual abstractive summarization.

CorrDetector: A Framework for Structural Corrosion Detection from Drone Images using Ensemble Deep Learning (2021)

Abdur Rahim Mohammad Forkan, Yong-Bin Kang, Prem Prakash Jayaraman, Kewen Liao, Rohit Kaul, Graham Morgan, Rajiv Ranjan, Samir Sinha, CorrDetector: A Framework for Structural Corrosion Detection from Drone Images using Ensemble Deep Learning, arXiv:2102.04686, 2021

TopicTracker: A Platform for Topic Trajectory Identification and Visualisation (2021)

Yong-Bin Kang, Timos Sellis, TopicTracker: A Platform for Topic Trajectory Identification and Visualisation, arXiv:2103.01432, 2021

ExpFinder: An Ensemble Expert Finding Model Integrating N-gram Vector Space Model and μCO-HITS (2021)

Yong-Bin Kang, Hung Du, Abdur Rahim Mohammad Forkan, Prem Prakash Jayaraman, Amir Aryani, Timos Sellis, ExpFinder: An Ensemble Expert Finding Model Integrating N-gram Vector Space Model and μCO-HITS, arXiv:2101.06821, 2021

This paper proposes an expert finding algorithm and currently under review in IEEE Transactions on Knowledge and Data Engineering.

An open-source framework for ExpFinder integrating N-gram vector space model and μCO-HITS (2021)

Hung Du, Yong-Bin Kang, An open-source framework for ExpFinder integrating N-gram vector space model and μCO-HITS, Software Impacts, Volume 8, 2021, 100069, ISSN 2665-9638, https://doi.org/10.1016/j.simpa.2021.100069.

This paper discusses the software details of the expert finding algorithm, named ExpFinder.

Boosting House Price Predictions using Geo-Spatial Network Embedding (2021)

Sarkar Snigdha Sarathi Das, Mohammed Eunus Ali, Yuan-Fang Li, Yong-Bin Kang, Timos Sellis, Boosting House Price Predictions using Geo-Spatial Network Embedding, Data Mining and Knowledge Discovery, 2021. https://doi.org/10.1007/s10618-021-00789-x

This paper proposes a house price pridiction algorithm using geo-spatial network embedding.

Methodology for refining subject terms and supporting subject indexing with taxonomy: A case study of the APO digital repository (2021)

Yong-Bin Kang, Jihoon Woo, Les Kneebone, Timos Sellis, Methodology for refining subject terms and supporting subject indexing with taxonomy: A case study of the APO digital repository, Decision Support Systems, Volume 146, 2021, 113542, ISSN 0167-9236, https://doi.org/10.1016/j.dss.2021.113542.

This paper proposes how to induce a subject term taxonomy from subject terms used to index digital documents.

Keyword Aware Influential Community Search in Large Attributed Graphs (2021)

Md. Saiful Islam, Mohammed Eunus Ali, Yong-Bin Kang, Timos Sellis, Farhana M. Choudhury, Keyword Aware Influential Community Search in Large Attributed Graphs, Information Systems, https://doi.org/10.1016/j.is.2021.101914, 2021

SRAuditor: An Automated Assessment Tool for Statement of Advice Documents (2022)

Yong-Bin Kang, Abdur Rahim Mohammad Forkan, Prem Prakash Jayaraman, Hung Du, Rohit Kaul, Dan Hunter. SRAuditor: An Automated Assessment Tool for Statement of Advice Documents, The 55th of Hawaii International Conference on System Sciences (HICSS), 2022

This paper proposes a semi-automatic approach for auditing financial Statements of Advice Documents.

Mapping community resources for disaster preparedness: humanitarian data capability and automated futures (2022)

McCosker, Anthony; Shaw, Frances; Calyx, Cobi; Kang, Yong-Bin; Albury, Kath; Australian Red Cross (2022). Mapping community resources for disaster preparedness: humanitarian data capability and automated futures. Analysis & Policy Observatory

This report details the rationale, background research and design for a platform to help local communities map resources for disaster preparedness. It sets out a first step in improving community data capability through resource mapping to enhance humanitarian action before disaster events occur. The project seeks to enable local community disaster preparedness and thus build community resilience by improving the quality of data about community strengths, resources and assets. In this report, the authors define a gap in existing humanitarian mapping approaches and the uses of open, public and social media data in humanitarian contexts. The report surveys current knowledge and present a selection of case studies delivering data and humanitarian mapping in local communities. Drawing on this knowledge and practice review and stakeholder workshops throughout 2021, the authors also define a method and toolkit for the effective use of community assets data.

Resilience in Online Mental Health Communities: Building a Resilience Dictionary with Semi-Automatic Text Analysis (2022)

Kang YB, McCosker A, Kamstra P, Farmer J. Resilience in Online Mental Health Communities: Building a Resilience Dictionary with Semi-Automatic Text Analysis. JMIR Formative Research. 18/08/2022:39013

This paper aims to create a resilience dictionary that reflects the characteristics and realisation of resilience within online mental health peer-support forums. The findings can be used to guide further analysis and inform strengths-based moderation and management of mental health forums.

ExpFinder: A hybrid model for expert finding from text-based expertise data (2022)

Yong-Bin Kang, Hung Du, Abdur Rahim Mohammad Forkan, Prem Prakash Jayaraman, Amir Aryani, Timos Sellis, ExpFinder: A hybrid model for expert finding from text-based expertise data, Expert Systems with Applications, Volume 211, ISSN 0957-4174, https://doi.org/10.1016/j.eswa.2022.118691.

This paper proposes an expert finding algorithm that can effectively find experts given expertise topics from text-based data.

Mobile IoT-RoadBot: An AI-powered Mobile IoT Solution for Real-Time Roadside Asset Management (2022)

Abdur Rahim Mohammad Forkan, Yong-Bin Kang, Felip Marti, Shane Joachim, Abhik Banerjee, Josip Karabotic Milovac, Prem Prakash Jayaraman, Chris McCarthy, Hadi Ghaderi, Dimitrios Georgakopolous. "Mobile IoT-RoadBot: An AI-powered Mobile IoT Solution for Real-Time Roadside Asset Management", The 28th Annual International Conference On Mobile Computing And Networking (ACM Mobicom 22)

Timely detection of roadside assets that require maintenance is essential for improving citizen satisfaction. Currently, the process of identifying such maintenance issues is typically performed manually, which is time consuming, expensive, and slow to respond. In this paper, we present Mobile IoT- RoadBot, a mobile 5G-based Internet of Things (IoT) solution, powered by Artificial Intelligence (AI) techniques to enable opportunistic real-time identification and detection of main- tenance issues with roadside assets.

Pre-sentence reports for Aboriginal and Torres Strait Islander people: an analysis of language and sentiment (2022)

Coulter, Darcy; Forkan, Abdur Rahim Mohammad; Kang, Yong-Bin; Trounson, Justin; Anthony, Thalia; Marchetti, Elena; Shepherd, Stephane (2022). Pre-sentence reports for Aboriginal and Torres Strait Islander people: an analysis of language and sentiment. Trends & issues in crime and criminal justice, 2022

Pre-sentence reports (PSRs) provide information to courts on an individual’s background, circumstances, risks, needs and plans. Research has found that PSRs focus heavily on risk of recidivism, while identification of prosocial cultural and community factors is limited. This study sought to describe the language and sentiment in these reports. We studied PSRs written for Aboriginal and/or Torres Strait Islander people sentenced by the mainstream County Court of Victoria and the Koori Court Division of the County Court of Victoria. Findings indicate that riskrelated words are more prevalent than words associated with strengths and culture in PSRs submitted to both courts. While the frequency of positive and negative sentiment was low in PSRs for both courts, those for the Koori Court were more positive in sentiment.

Culture, Strengths, and Risk: The Language of Pre-Sentence Reports in Indigenous Sentencing Courts and Mainstream Courts (2023)

Coulter, D. J., Forkan, A. R. M., Kang, Y.-B., Trounson, J. S., Anthony, T., Marchetti, E., & Shepherd, S. M. (2023). Culture, Strengths, and Risk: The Language of Pre-Sentence Reports in Indigenous Sentencing Courts and Mainstream Courts. Criminal Justice and Behavior, 50(1), 76–100.

Pre-sentence reports (PSRs) provide important information about an individual’s background and circumstances to assist judicial officers in the sentencing process. The present study analyzed PSRs for 63 Aboriginal and Torres Strait Islander people sentenced by either an Indigenous sentencing court or a mainstream court in the Australian State of Victoria. Using natural language processing techniques, our analyses revealed few differences between PSRs conducted for each court. However, PSRs were found to predominantly feature key words that are risk-based, with mainstream court PSRs more negatively worded than the Indigenous sentencing court’s PSRs. This may have been due to the inclusion of results from a risk and need assessment tool. Pro-social factors did comprise more than one third of extracted keywords, although the number of strength-based culture-related keywords, in particular, was low across PSRs in both courts. It is possible that courts may not be receiving all the information needed to promote individualized justice.

TopicTracker: A platform for topic trajectory identification and visualisation (2023)

Yong-Bin Kang and Timos Sellis (2023). TopicTracker: A platform for topic trajectory identification and visualisation. SoftwareX, 22, 101330.

Topic trajectory information provides crucial insight into the dynamics of topics and their evolutionary relationships over a given time. Also, this information can improve our understanding on how new topics have emerged or formed through a sequential or interrelated events of emergence, modification and integration of prior topics. Nevertheless, the implementation of the existing methods for topic trajectory identification is rarely available as usable software. In this paper, we present TopicTracker, a platform for topic trajectory identification and visualisation. The key of TopicTracker is that it can represent the three facets of information together, given two kinds of input: a time-stamped topic profile consisting of the set of the underlying topics over time, and the evolution strength matrix among them: evolutionary pathways of dynamic topics, evolution states of the topics, and topic importance. TopicTracker is a publicly available software implemented using the R software.

VideoDL: Video-Based Digital Learning Framework Using AI Question Generation and Answer Assessment (2023)

Forkan, Abdur Rahim Mohammad; Kang, Yong-Bin; Jayaraman, Prem Prakash; Du, Hung; Thomson, Steven; Kollias, Elizabeth; Wieland, Natalie (2023). VideoDL: Video-Based Digital Learning Framework Using AI Question Generation and Answer Assessment. International Journal of Advanced Corporate Learning (iJAC), vol 16, pp. 19-27.

Assessing learners’ understanding and competency in video-based digital learning is time-consuming and very difficult for educators, as it requires the generation of accurate and valid questions from pre-recorded learning videos. This paper demonstrates VideoDL, a video-based learning framework powered by Artificial Intelligence (AI) that supports automatic question generation and answer assessment from videos. VideoDL comprises of various AI algorithms, and an interactive web-based user interface (UI) developed using the principles of human-centred design. Our empirical evaluation using real-world videos from multiple domains demonstrates the effectiveness of VideoDL.

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