Keynote Speaker of ICOK2017 



Prof. Shuliang Li
PhD, Life Fellow of the British Computer Society, IEEE member
Reader in Business Information Management & Systems Westminster Business School
University of Westminster, United Kingdom
(Also Sichuan 100-Talent Scheme Visiting Professor at Southwest Jiaotong University, China)

Biography: Prof. Dr Shuliang Li is a Reader in Business Information Management at Westminster Business School, University of Westminster. He acted as BIM&O departmental Research Leader from January 2010 to July 2013.
Prof. Li is also Sichuan 100-Talent Scheme Visiting Professor in China for research collaboration purposes. He is a life Fellow of the British Computer Society (FBCS).
(Official Web page:

Title of Speech: Data, Information, Knowledge, Models & Systems for Decision Making in the Digital Age

Abstract: This keynote speech is concerned with data, information, knowledge, models & systems for decision making in the digital age. In particular, the following topics will be covered.
- Safety supervision of coal mining in China: Adapted evolutionary jungle model, and simulation and analysis of the co-evolution process of the interaction between supervision agents and the enterprise agents, and investigate how the stable coexistence equilibrium point can be reached, with validation conducted using safety supervision data and China coal mine mortality rate per million ton From 1949 to 2015. (Dingxuan Huang, Shuliang Li & David Barnes)
- Density game analysis of supply side symbiosis behaviour of green buildings considering the construction market carrying capacity (Dingxuan Huang & Shuliang Li)
- Agent-based dynamic simulation of the dynamic evolution of enterprise carbon assets, heterogeneity analysis of the management strategies for enterprise carbon assets, and enterprise carbon management from the perspective of carbon asset value space (Yin Zeng, Shuliang Li, et al.)
- Open innovation: intelligent model, social media & complex adaptive system simulation (Shuliang Li, Jim Zheng Li, et al.) - A framework, model and software prototype for modelling and simulation for deshopping behaviour and how companies respond (Shawkat Rahman & Shuliang Li)
- Integrating multiple agents, simulation, knowledge automation and fuzzy logic for international marketing decision making (Shuliang Li & Jim Zheng Li)
- A Web-based hybrid intelligent system & knowledge automation for combined conventional, digital, mobile, social media and mobile marketing strategy formulation (Shuliang Li, Jim Zheng Li, et al.)
- A hybrid intelligent model for Web & social media dynamics, and evolutionary and adaptive branding (Shuliang Li)
- A hybrid paradigm for modelling, simulation and analysis of brand virality in social media (Shuliang Li & Jim Zheng Li)
- Investigating survivability of configuration management tools in unreliable and hostile networks (Tero Karvinen & Shuliang Li)  


Prof. Yan Yang PhD, IEEE/ACM Member
Vice Dean of School of Information Science & Technology, Southwest Jiaotong University, China
Vice Chair of ACM Chengdu Chapter
Key Lab of Cloud Computing & Intelligent Technology, Sichuan Province

Biography: Prof. Yan Yang is currently Professor and vice dean of Information Science and Technology, Southwest Jiaotong University. She worked as a visiting scholar at the Center of Pattern Analysis and Machine Intelligence (CPAMI) in Waterloo University of Canada for one and half year. She is an Academic and Technical Leader Candidate of Sichuan Province. She has participated in more than 10 high-level projects recently, such as five programs supported by the National Natural Science Foundation of China (NSFC). Prof. Yang has authored and co-authored over 130 papers in journals and international conference proceedings. She also serves as the Vice Chair of ACM Chengdu Chapter, Member of IEEE, Senior Member of CCF and CAAI, Member of CCF Artificial Intelligence and Pattern Recognition, Member of CAAI Machine Learning, Deputy Secretary General of Sichuan Province Computer Society.
(Official Web page:

Title of Speech: Multi-view Learning and Clustering for Big Data  

Abstract: Multi-view learning and clustering, which provides a hopeful way to seek a category of the data with multiple views, has attracted considerable attention in recent years. However, many existing methods are not yet adapted to the challenges of big data, which is much more complicated than ever before with multi-view characteristics. By exploiting the complementary and consistency information from multi-view data, the multi-view clustering aims to get better clustering quality rather than rely on the individual view. The main challenge is how to integrate this information and give a compatible clustering solution across multiple views. In this talk, I will discuss multi-view learning and clustering, and also give several examples of mining big data being conducted in my research group.  



Prof. Alan Eardley
Staffordshire University, United Kingdom

Biography: Professor W. Alan Eardley was born in Stoke-on-Trent, England in 1949. Through part-time study as a mature student, he obtained a B.A. in Business Studies with first class honours in 1984 and a Master’s Degree in Computer Science from Aston University in the U.K. in 1989. His PhD, in Strategic Information Systems from Southampton University in the U.K., supervised by Professor David Avison, was awarded in 2001. Alan is Professor of Enterprise Computing in the School of Computing at Staffordshire University in the U.K. and is an Adjunct Professor at Asia Pacific University of Technology and Innovation in Kuala Lumpur. Professor Eardley researches, publishes and supervises PhD students in knowledge management and IT strategy and teaches research methods. He is a long-standing Member of the British Computer Society and is a Chartered IT professional.





Plenary Speakers


Lili Yang
PhD Reader in Information Systems and Emergency Management School of Business & Economics Loughborough University, United Kingdom

Biography: Lili Yang is a reader in the School of Business & Economics at Loughborough University. She received her MSc (Loughborough) and PhD (Derby) in the UK, joining the school in September 2006 as a lecturer. Prior to this appointment, she worked in the Computer Science department at Loughborough University as a part-time lecturer (2004-2006), and in the Applied Computing department at the University of Derby as a lecturer/senior lecturer (2001-2004). She is a fellow of the British Computer Society (FBCS) and a Chartered IT Professional (CITP).
Lili has taught a variety of modules on computing and business at both postgraduate and undergraduate levels including Business Analysis for Decision Making, Quantative Methods for Business, Information Systems Development, Business Information Management, Business Modelling, From Networks to Internet, Programming, Data Communications etc. Currently she is the program director of MSc Business Analysis and Management. She has played the role as an external examiner for UG and PG teaching programs for a number of universities in the UK.
Lili has conducted a significant amount of research both independently and working in team. As the principal investigator she has led 12 projects and carried out 4 projects as co-investigator. The total budget has reached to over £3 million. She has published over 70 journal papers, conference papers and books. Recent publications appear in quality journals such as Information Systems Research, European Journal of Operational Research, Technological Forecasting and Social Changes, IEEE Transactions on Systems, Man, and Cybernetics (SMC), to be named.
(Official Web page: ).

Title of Speech: A Novel Approach for Cascading Disaster Events Modeling

Abstract: Cascading disasters are disasters that occur as a direct or indirect result of an initial disaster. These cascading disasters spread disruptions in complex ways that makes them difficult to comprehend and challenging to deal with. A well-known example of such cascading disasters is the meltdown of Fukushima’s nuclear reactors, after the power plant was hit by a tsunami on 11 March 2011, which in turn was triggered by an earthquake. In order to protect, detect, react and recover effectively from natural disasters it is essential to obtain better modeling of the propagation of the cascading disasters. Many of disaster modeling techniques display weaknesses in terms of scalability, flexibility and usability, and some of them have led to disastrous consequences in practice.
In this talk, we treat cascading natural disasters as a typical Markov Chain process which describes cascading disasters that follow a chain of linked events, where what happens next depends only on the current state of the trigger disaster. We recognized that most of experts would refuse to give or agree on precise ratio-scale event probability estimates due to lack of sufficient evidence, but they feel confident to rank a list of events according to their likelihood of occurrence. The method for eliciting probabilities is named as the rank-order centroid (ROC) method.
We propose here that cascading disasters could be described as a Markov Chain model, with an initial probability being given by a ROC method, and the probability of transition from the current state to a next state being given by the CIA-ISM method. This scenario-based cascading disaster modeling approach is expected to handle social factors, environmental factors and disaster events as well as interacting relationships to estimate the evolution of a natural disaster. Therefore a better understanding and forecasting of the impacts of a sudden environmental hazard on a cascade of crises could be achieved. 






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