Computational Fair Division: Towards a Fairer World
Fair Division is a rich field that has had contributions from various branches of the social sciences. Fairness is a central requirement when making collective decisions, allocating tasks, dividing resources, forming representative committees, or splitting costs. In computer science, as we endeavour to build new multi-agent systems, mechanisms, and protocols, fairness comes up as an important concern as well. In this talk, I will discuss the exciting interaction between computer science and fair division. In particular, I will talk about recent work on so called “cake cutting algorithms”.
Haris Aziz is a senior research scientist at Data61, CSIRO and a conjoint senior lecturer at the University of New South Wales. Aziz completed his PhD from University of Warwick, MSc from University of Oxford and his BSc (Honours) from Lahore University of Management Sciences. He undertook postdoctoral research at the Ludwig Maximilian University of Munich and Technical University of Munich in Germany.
His research interests lie at the intersection of artificial intelligence, theoretical computer science, and economics—especially computational social choice and algorithmic game theory.
Aziz is a recipient of the CORE 2017 Chris Wallace Award and the Julius Career Award (2016 – 2019). In 2015, he was selected by the Institute of Electrical and Electronics Engineers (IEEE) for the AI 10 to Watch List. He was also selected for the Early Career Spotlight list at IJCAI 2016.
Aziz has published in computer science (AAAI, AAMAS, AIJ, IJCAI, JAIR, FOCS, and STOC), economics (Games and Economic Behavior, Economics Letters, Journal of Mathematical Economics, and Social Choice & Welfare), as well as the primary venues at the intersection of the two fields (such as ACM EC, SAGT, and WINE).
He is on the editorial board of Journal of Artificial Intelligence Research and has served as the co-chair of workshops CoopMAS 2013 and Explore 2015-2016-2017. In 2015, he was an invited professor at University Paris Dauphine.
Connecting through feedback
As a teacher of primarily first year students, the growing disconnect between the academic requirements of the discipline and the academic capital of many students has become more and more apparent to me. This disconnect has been particularly evident in a consistent decline in students’ performance in assessments which in many cases has led to subsequent attrition from the program.
The responsibility to find ways to address this disconnect therefore seemed (at least, in part) to lie with me. After receiving an end of semester Student Evaluation of Course (SEC) survey score of 3.6 out of 5 for Question 2: “The assessment was clear and fair” in Semester 1, 2010, for an introductory computer architecture course, it was clear to me that focusing on assessment and feedback was a good place to start. In particular, in order to understand the needs of the students and attempt to address the perceived deficits, much richer and more timely feedback than the formal end of semester course survey was required.
This presentation will discuss a number of innovative practices for connecting with students by eliciting feedback in a timely manner and closing the feedback loop. It will discuss a model for developing a local culture in which academics are invited to share information about their students’ course experiences. By “Connecting through Feedback” in this way, students become empowered to take control of their own learning, and staff are empowered to break down barriers to collegial engagement by cooperatively exploring common issues and shared solutions.
Dr Sven Venema is a senior lecturer in the School of Information and Communication Technology at Griffith University. Dr Venema transcends the decline in educational capital of commencing IT students by creating supportive learning environments through comprehensive, innovative approaches to assessment and feedback. His unique, student-centred teaching practices empower students to take control of their own learning, cater to a diverse range of student needs and increase student confidence and engagement. Dr Venema’s original teaching methods are informed by scholarly research and have been the subject of conference presentations and journal articles.
Fuzzy Transfer Learning for Prediction
This presentation highlights the value of fuzzy transfer learning methods and related algorithms for handling complex prediction problems in rapidly-changing data distribution and data-shortage situations. It provides a framework for utilising previously-acquired knowledge to predict new but similar problems quickly and effectively supported by fuzzy system techniques. It systematically presents the developments in fuzzy transfer learning methods, including fuzzy domain adaptation and fuzzy cross-domain adaptation, for classifications and regression
Professor Jie Lu is a scientist in the area of decision support systems, recommender systems, fuzzy transfer learning, and concept drift. She is the Associate Dean (Research) in the Faculty of Engineering and Information Technology at the University of Technology Sydney (UTS). She is also the Director of the Decision Systems and e-Service Intelligence (DeSI) Research Laboratory in Cneter of QCIS. She has published six research books and 400 papers in international journals (such as Artificial Intelligence, IEEE Transactions on Fuzzy Systems, Decision Support Systems) and conference proceedings. She has won eight Australian Research Council (ARC) discovery grants and 10 other research grants in the last 15 years. She serves as the Editor-in-Chief for Knowledge-Based Systems (Elsevier) and the Editor-in-Chief for International Journal on Computational Intelligence Systems (Atlantis), and an ARC College of Expert. She has delivered 15 keynote speeches at international conferences, organised 12 special issues in international journals and chaired 10 international conferences.
Geolocation Understanding in Social Media for Point-of-interest Recommendation
Location data has been playing an important role in many social media applications, particularly the location-based services. However, publicity-visible location annotations are remarkably sparse in social media data, such as online images. In this talk, we introduce novel methods to estimate missing locations for social images by effectively fusing multi-modalities of social media data. Interestingly, by integrating visual data and location data, such as geo-tagged images and check-ins, important location proximities can be better understood and discovered, which will further facilitate user behaviour analytics.
Dr Zi Huang received her BSc degree from Tsinghua University, China, in 2001, and her PhD in Computer Science from the University of Queensland, Australia, in 2007. She is currently an ARC Future Fellow with the School of Information Technology and Electrical Engineering, University of Queensland. Her research interests include multimedia indexing and search, social data analysis and knowledge discovery.
Security and Privacy in Passive RFID Systems
Radio Frequency Identification (RFID) enables the automatic identification of objects using radio waves without the need for physical contact with the objects. RFID has been widely used in various fields such as logistics, manufacturing, pharmaceutical, supply chain management, healthcare, defense, aerospace and many other areas, apart from touching our everyday lives through RFID enabled car keys, ePassports, clothing, electronic items and others. However, the wide adoptions of RFID technologies also introduce serious security and privacy risks as the information stored in RFID tags can easily be retrieved by any malicious party with a compatible reader. In this talk we will introduce some security and privicy challenges in passive RFID technologies, and based on our research, we will outline a number of schemes for authentication, ownership transfer, secure search and grouping proof in passive mobile RFID systems.
Professor Wanlei Zhou received the B.Eng and M.Eng degrees from Harbin Institute of Technology, Harbin, China in 1982 and 1984, respectively, and the PhD degree from The Australian National University, Canberra, Australia, in 1991, all in Computer Science and Engineering. He also received a DSc degree (a higher Doctorate degree) from Deakin University in 2002. He is currently the Alfred Deakin Professor (the highest honour the University can bestow on a member of academic staff), Chair of Information Technology, and Associate Dean (International Research Engagement) of Faculty of Science, Engineering and Built Environment, Deakin University. Professor Zhou has been the Head of School of Information Technology twice (Jan 2002-Apr 2006 and Jan 2009-Jan 2015) and Associate Dean of Faculty of Science and Technology in Deakin University (May 2006-Dec 2008). Before joining Deakin University, Professor Zhou served as a lecturer in University of Electronic Science and Technology of China, a system programmer in HP at Massachusetts, USA; a lecturer in Monash University, Melbourne, Australia; and a lecturer in National University of Singapore, Singapore. His research interests include distributed systems, network security, bioinformatics, and e-learning. Professor Zhou has published more than 300 papers in refereed international journals and refereed international conferences proceedings. He has also chaired many international conferences. Prof Zhou is a Senior Member of the IEEE.
Understanding change in complex environments
When we monitor a complex system, often we are more interested in what has changed, rather than what remains the same. Unsupervised methods in machine learning and data mining have made great progress in building models to explain complex data sets. For example, clustering methods can provide insights into the inherent structure or distribution of data for diverse applications such as market research or bioinformatics. However, traditional unsupervised learning methods tend to focus on building models of static snapshots of a data set. They don’t explicitly learn models of the dynamics of the underlying system. In particular, for many applications it is important to learn what has changed between successive snapshots of a system, such as how a customer behaviour has changed as a result of a marketing campaign, or how network traffic has changed as a result of a cyber attack. This presentation will give an overview of our contributions to the challenge of understanding change in data mining.
Chris Leckie is a Professor with the Department of Computing and Information Systems at the University of Melbourne. He has over two decades of research experience in artificial intelligence and data mining, having led research teams at Telstra Research Laboratories, NICTA and the University of Melbourne. His research on using data mining for anomaly detection, fault diagnosis, cyber-security and the life sciences has led to a range of operational systems used in industry, as well as over 200 articles published in leading international conferences and journals. His work on filtering denial-of-service attacks on the Internet resulted in a commercial product that was developed a local company and sold overseas. He is currently Associate Director of the new Oceania Cyber Security Centre.