Monday, December 21, 2009

Dec 2009 ISAmI 2010

I've been invited to serve on the program committee for the 1st International Symposium on Ambient Intelligence to be held in GuimarĂ£es, Portugal on the 16th-18th June, 2010 (

[ Website ] [ Call for papers ]

"Ambient Intelligence (AmI) is a recent paradigm emerging from Artificial Intelligence (AI), where computers are used as proactive tools assisting people in their day-to-day, making everyone’s life more comfortable. The interaction with computers is changing quickly, as we no longer need to do it in ways not natural for us, since a main concern of AmI consists in to make possible the interaction with computational systems using friendly interfaces, allowing input through natural language or simple gestures.

This inclusion of technology in our day-to-day objects and environments should be as invisible as possible, because of the computational power and communication techno-logies embedding in most of the devices we use nowadays. Human interaction with computing power embedded systems should happen without noticing it. The only awareness people should have arises from AmI: more safety, comfort and well-being, emerging in a natural and inherent way.

As defined by the IST Advisory Group (ISTAG), AmI has born thanks to three new key technologies: Ubiquitous Computing, Ubiquitous Communication and Intelligent User Interfaces, which are starting to change the way we see computers. ISAmI is the International Symposium on Ambient Intelligence, aiming to bring together researchers from various disciplines that constitute the scientific field of Ambient Intelligence to present and discuss the latest results, new ideas, projects and lessons.

Thursday, December 10, 2009

Dec 2009 HITLab Australia Director

I have now started as director of the Human Interface Technology Laboratory Australia (HIT Lab AU). I am also now an Associate Professor in the School of Computing and Information Systems at the University of Tasmania. I aim to build an exciting research and teaching environment in the HITLab Australia for undergraduates, postgraduates, postdocs, research interns, researchers, collaborators and all our industry partners.

My role is to provide strategic leadership of the HIT Lab AU and its inter-disciplinary undergraduate and postgraduate courses and research higher degree programs. As Director I will oversee exciting, cross-disciplinary collaboration in teaching and research activities with other UTAS schools and faculties; the development of consulting activities and commercial projects with business and industry.

I will also oversee the establishment of national and international partnerships with our partners the HIT Labs US and NZ, the Virtual Worlds Consortium, and other organisations. My aim is to make this a major research and teaching centre on the national and international stage.

My new contact details are:

Associate Professor Aaron Quigley
Director of the Human Interface Technology Laboratory (HITLab) Australia
School of Computing and Information Systems
University of Tasmania
Locked Bag 1359
Launceston Tas 7250


phone: +61 3 6324 3977

Tuesday, December 01, 2009

Dec 2009 Chapters in book on Mining and Analysing Social Networks

Along with two of my graduate students we have had two book chapters accepted in the upcoming book entitled "Mining and Analyzing Social Networks" which is part of the book series of studies in Computational Intelligence, Springer-Verlag, Heidelberg Germany, 2010. Social Network Analysis and Visualization will form an aspect of collaborative and emerging visualization research projects within the Human Interface Technology Research Laboratory Australia (HITLAB AU).

These chapters are entitled "Actor Identification in Implicit Relational Data" and "Perception of Online Social Networks" which are detailed below.

Actor Identification in Implicit Relational Data
Michael Farrugia and Aaron Quigley

Large scale network data sets have become increasingly accessible to researchers. While computer networks, networks of webpages and biological networks are all important sources of data, it is the study of social networks that is driving many new research questions. Researchers are finding that the popularity of online social networking sites may produce large dynamic data sets of actor connectivity.

Sites such as Facebook have 250 million active users and LinkedIn 43 million active users. Such systems offer researchers potential access to rich large scale networks for study. However, while data sets can be collected directly from sources that specifically define the actors and ties between those actors, there are many other data sources that do not have an explicit network structure defined. To transform such non-relational data into a relational format two facets must be identified - the actors and the ties between the actors. In this chapter we survey a range of techniques that can be employed to identify unique actors when inferring networks from non explicit network data sets.We present our methods for unique node identification of social network actors in a business scenario where a unique node identifier is not available. We validate these methods through the study of a large scale real world case study of over 9 million records.

Perception of Online Social Networks
Travis Green and Aaron Quigley

This paper examines data derived from an application on that investigates the relations among members of their online social network. It confirms that online social networks are more often used to maintain weak connections but that a subset of users focus on strong connections, determines that connection intensity to both connected people predicts perceptual accuracy, and shows that intra-group connections are perceived more accurately. Surprisingly, a user‘s sex does not influence accuracy, and one‘s number of friends only mildly correlates with accuracy indicating a flexible underlying cognitive structure. Users‘ reports of significantly increased numbers of weak connections indicate increased diversity of information flow to users. In addition the approach and dataset represent a candidate ―ground truth‖ for other proximity metrics. Finally, implications in epidemiology, information transmission, network analysis, human behavior, economics, and neuroscience are summarized. Over a period of two weeks, 14,051 responses were gathered from 166 participants, approximately 80 per participant, which overlapped on 588 edges representing 1341 responses, approximately 10% of the total. Participants were primarily university-age students from English-speaking countries, and included 84 males and 82 females. Responses represent a random sampling of each participant‘s online connections, representing 953,969 possible connections, with the average participant having 483 friends. Offline research has indicated that people maintain approximately 8-10 strong connections from an average of 150-250 friends. These data indicate that people maintain online approximately 40 strong ties and 185 weak ties over an average of 483 friends. Average inter-group accuracy was below the guessing rate at 0.32, while accuracy on intra-group connections converged to the guessing rate, 0.5, as group size increased.