Keynote    Speakers


The following keynote speakers have been invited to give keynote talks:


Keynote speech by Professor Biswas:

Robust Model-based Diagnosis of Dynamic Systems

The proliferation of safety-critical embedded systems has created great demands for online monitoring and fault diagnosis techniques. The goal of model-based diagnosis is to monitor system behavior, and use models to quickly detect and isolate the cause when deviations from system behavior are detected. A number of methodologies have been proposed, but the implementation of on-line schemes that integrate fault detection, fault isolation, and fault identification remains challenging. Moreover, model-based diagnosis methods can be computationally expensive, thus making online diagnosis schemes infeasible.


In this talk, I will review different model-based methods that have been developed for detection and isolation of faults in dynamic systems. Then I will present our innovative approach for diagnosis that combines the use of qualitative fault signatures derived from a causal model of dynamic system behavior to quickly isolate faults in complex dynamic systems. As a next step, we use our modeling approach to design a scheme using Dynamic Bayes Nets (DBNs) for robust diagnosis of dynamic systems. DBNs provide a systematic method for modeling the behavior of dynamic systems in uncertain environments that includes both measurement noise and model uncertainties.  In the last part of the talk, I will discuss some recent results on extending the DBN schemes for distributed diagnosis. Experimental results will be presented to demonstrate the effectiveness of the fault diagnosis schemes.

Gautam Biswas is a Professor of Computer Science and Computer Engineering in the EECS Department and a Senior Research Scientist at the Institute for Software Integrated Systems (ISIS) at Vanderbilt University. He has a Ph.D. degree in Computer Science from Michigan State University in E. Lansing, MI. Prof. Biswas conducts research in Intelligent Systems with primary interests in hybrid modeling, simulation, and analysis of complex embedded systems, and their applications to diagnosis and fault-adaptive control. As part of this work, he has worked on fault-adaptive control of fuel transfer systems for aircraft, and Advanced Life Support systems and the ADAPT power distribution testbed for NASA. He has also initiated new projects in distributed monitoring and diagnosis and prognosis and health management of complex systems. A second area of research is in planning and scheduling of tasks in complex applications. He has applied his planning and scheduling algorithms for robotic task planning in uncertain environments, and dynamic resource allocation in distributed real-time environments. In other research projects, he is involved in developing simulation-based environments for learning and instruction. His research has been supported by funding from NASA, NSF, DARPA, AFOSR, the US Department of Education, Honeywell, and Boeing Phantom Works. He has published extensively, and has over 300 publications. He is an Associate Editor of the IEEE Transactions on Systems, Man, and Cybernetics, Part A, the International Journal of Prognostics and Health Management, and the Educational Technology and Society journal. He has served on the Program Committee of a number of conferences, and most recently was Program co-chair of the 18th International Workshop on Principles of Diagnosis. He is a senior member of the IEEE Computer Society, ACM, AAAI, and the Sigma Xi Research Society. 


Keynote speech by Professor Kotagiri:

Data Mining for Role Based Access Control

Role Based Access Control (RBAC) is an efficient and effective mechanism to manage and govern access permissions to objects for a large number of users. The assignment of permissions through roles reduces administration costs and assists with policy enforcement in large enterprises. The benefits offered by RBAC drive its popularity in industry. However, migration to RBAC from existing access control methods is not a simple task for established environments. One of the challenges, and the most costly component of migration, is role engineering: determining of a set of roles that accurately reflects the internal functionalities and workings of the enterprise. Existing automated and semi-automated approaches are not sufficient in producing a comprehensive set of roles. The process is time consuming, error prone and costly if manual elicitation is used.

In this talk I will address the issue of effectively evaluating roles and provide solutions for identifying  effective roles for access control using graph based and data miming techniques. First, Permission Set Mining (PSM) is proposed to identify the set of roles that cover the most user-to-role assignments. These are the most important roles as they offer the largest coverage of permissions. Second, to identify ideal RBAC configurations using our proposed graph based role engineering; we propose an iterative optimisation to show that the graph based model is capable of evaluating both roles and their relationships with users and permissions. We then propose the more scalable algorithm, named RoleAnneanling, for identifying best role graph structure. Finally, we propose RoleVAT, a visualisation technique that can be used to monitor role coherence in a given RBAC configuration and identify user and permission tendencies


Professor Ramamohanarao (Rao) Kotagiri is received his degrees BE at Andhra University, ME at the Indian Institute of Science, Bangalore and PhD at Monash University. He was awarded the Alexander von Humboldt Fellowship in 1983. He has been at the University Melbourne since 1980 and was appointed a professor in computer science in 1989.

Rao held several senior positions including Head of Computer Science and Software Engineering, Head of the School of Electrical Engineering and Computer Science at the University of Melbourne, Deputy Director of Centre for Ultra Broadband Information Networks, Co-Director of the Key Centre for Knowledge-Based Systems, and Research Director for the Cooperative Research Centre for Intelligent Decision Systems. He served as a member of the Australian Research Council Information Technology Panel. He served on the Prime Minister's Science, Engineering and Innovation Council working party on Data for Scientists. He also served on the Editorial Boards of the Computer Journal. At present he is on the Editorial Boards for Universal Computer Science, the Journal of Knowledge and Information Systems, IEEE TKDE (Transactions on Knowledge and Data Engineering), Journal of Statistical Analysis and Data Mining and VLDB (Very Large Data Bases) Journal. He served as a program committee member of several International conferences including SIGMOD, IEEE ICDM, VLDB, ICLP and ICDE. He was the program Co-Chair for VLDB, PAKDD, DASFAA and DOOD conferences. He is a steering committee member of IEEE ICDM, PAKDD and DASFAA. Rao is a Fellow of the Institute of Engineers Australia, Australian Academy Technological Sciences and Engineering and Australian Academy of Science. Rao has research interests in the areas of Database Systems, Logic Based Systems, Agent Oriented Systems, Information Retrieval, Data Mining, Intrusion Detection and Machine Learning.


Keynote speech by Professor Yang:

Transfer Learning for Activity Recognition through Sensor Data

With the proliferation of various sensor data, how to effectively recognize users¡¯ activities to offer timely services becomes a critical issue.  Sensor-based activity recognition has a wide range of applications, including health monitoring and mobile commerce. However, sensor data are inherently noisy, incomplete and dynamic in nature, making the activity recognition a very challenging problem.  In this talk, I will describe how we use transfer learning to solve this problem.  Transfer learning happens when the knowledge learned from one domain can benefit learning in other domains, and transfer-learning research has captivated researchers from across a wide spectrum of areas.  When applied to activity recognition problems, knowledge gained from other domains can help lift the performance even when the target domain data are incomplete, sparse and noisy.  We illustrate this technique in several application domains, including indoor activity tracking and outdoor, large-scale activity recommendation.

Qiang Yang is a professor in the Department of Computer Science and Engineering, Hong Kong University of Science and Technology and an IEEE Fellow. His research interests are artificial intelligence, including automated planning, machine learning and data mining.  He graduated from Peking University in 1982 with BSc. in Astrophysics, and obtained MSc. degrees in Computer Science and in Astrophysics from the University of Maryland, College Park in 1985 and 1987, respectively, as well as his PhD in Computer Science from the University of Maryland, College Park in 1989.  He was an assistant/associate professor at the University of Waterloo between 1989 and 1995, and a professor and NSERC Industrial Research Chair at Simon Fraser University in Canada from 1995 to 2001.  He is a fellow of IEEE, and a member of AAAI, AAAS and ACM. He is an author of two books and over 200 publications on AI and data mining. His research teams won the 2004 and 2005 ACM KDDCUP international competitions on data mining.  He is an invited speaker at IJCAI 2009, ACL 2009 and ACML 2009.


Qiang Yang is on the editorial boards of several international journals.  He is the founding Editor in Chief of the ACM Transactions on Intelligent Systems and Technology (ACM TIST).  He is on the editorial board of IEEE Intelligent Systems and Journal of Web Intelligence.  Previously he has been an associate editor for IEEE Transactions on Knowledge and Data Engineering, and Journal of Knowledge and Information Systems. He has been an organizer for several international conferences in AI and data mining, including the PC co-chair for ACM KDD 2010, the conference co-chair for ACM IUI 2010, Tutorial co-chair for AAAI 2005/2006, Workshop chair for ACM KDD 2007, program co-chair for PRICAI 2006 and PAKDD 2007, data mining contest chair for IEEE ICDM 2007/2009, vice chair for ICDM 2006 and CIKM 2009, conference chair for ICCBR 2001 and PC co-chair for Canadian AI conference in 2000.