Associating Web content with geography has been a topic of growing interest over the past few years because of the local aspects of Web search and the increasing number of applications and services that benefit from geotagged content. An underlying question is how geographical variation is embodied in content and if a geographical scope can be detected.
This talk gives an overview of the work in this area, showcasing some of our own work. In particular, we showcase our work in the areas of (1) toponym resolution -- assigning a location mention in an online document to a geographical referent, i.e. latitude and longitude, (2) associating geographic foci to more general classes of named entities, and (3) detecting the scope of geographic locations in a geographical database (aka gazetteer).
Davood Rafiei did his bachelor in Sharif University (Iran), his M.Sc. in Waterloo and his PhD in Toronto. He is currently an Associate Professor of Computer Science and member of the Database Systems Research Group at the University of Alberta. Dr. Rafiei has served in the program committees of both database conferences such as SIGMOD and VLDB and Web conferences such as WWW. His areas of interest also span over databases and the Web and include integrating natural language text with relational data, Web information retrieval and similarity queries and indexing. Dr. Rafiei was a visiting scientist at Google (Mountain View) for a year between 2007-2008, a visiting professor at Kyoto University in 2014 and a visiting professor at the University of Paris (Descartes) in 2015.
Filter and channel theory play essential roles in modeling many engineering problems. A channel can be a transmission line, computer memory, a learning machine, or a compression algorithm. We spend a great deal of effort to design trustworthy channels or filters. Trustworthy channels don’t lie, don’t betray the source, or mislead the destination. But these all are based on one strong assumption: source is good, trustworthy, and honest. Almost in all models, the honesty of the source is out of context. In the case of natural and man-made sources such as sensors, engineers model and estimate sampling and measurement error. But dishonesty is different from error. In social context, error is an honest mistake but with good faith. Error is not because of bad intention and mostly caused by poor judgement.
Dishonesty has two features to be detected: insincere intention and lack of fairness. Unfortunately in most social and human behavior studies which are based on data collection from questionnaires and surveys, these two variables are hard to measure. As a matter of fact, we humans demonstrate our true intentions and honest opinion when we unintentionally express our views. It means we don’t manipulate our thought for the sake of interests or to prevent any threat. But most lab experiments, surveys, and questionnaires are based on this fact that participants know they are under study.
In this talk, first the theory of Honest Data is informally introduced and discussed and then we show that Social Data is honest. This is where Social Computing and Human Computation come to play. Some flagship projects are introduced and a summary of our research group is presented.
Masoud Makrehchi received the B.S. degree in Electrical and Computer Engineering from Iran University of Science and Technology, Tehran, Iran, in 1991. He received the M.S. degree in Computer Engineering from Shiraz University, Shiraz, Iran, in 1994. From 1991 to 2002, he served in several positions in research and industry working on intelligent information systems and software engineering. In 2007, he received his Ph.D. in Electrical and Computer Engineering from University of Waterloo, Canada. He continued his research as Postdoc research associate on intelligent systems and data mining in PAMI lab at the University of Waterloo from Aug. 2007 to April 2008. He joined Thomson Reuters R&D department based in Greater Minneapolis-Saint Paul Area, MN in May 2008 as senior research scientist. Since July 2012, he is with the University of Ontario Institute of Technology (UOIT), Ontario, Canada, as associate professor. His current primary research interests are in the areas of recommender systems, text and data mining, machine learning, social computing, sport analytics and legal analytics.
Cloud Computing is a successful paradigm for delivering computing resources residing in providers’ data centers as a service over the Internet to the public. This talk covers three recent studies in cloud computing including pricing, energy efficiency, and networking in clouds. The first study identifies an auction mechanism for pricing spot instances. The second study shows how geographical load balancing can be utilized to maximize the use of renewable energy sources in cloud data centers, and the last but not the least study represents an architecture and design of a low-cost testbed for conducting research on software-defend clouds. Finally, the keynote discusses the challenges and opportunities of emerging cloud technologies and opens up pathways for future research.
Adel Nadjaran Toosi is a Post-doctoral Research Fellow at the Cloud Computing and Distributed Systems (CLOUDS) Laboratory, School of Computing and Information Systems (CIS), University of Melbourne, Australia. He received his B.Sc. degree in 2003 and his M.Sc. degree in 2006 both in Computer Science and Software Engineering from Ferdowsi University of Mashhad, Iran and his Ph.D. degree in 2015 from the University of Melbourne. Adel’s Ph.D. studies were supported by International Research Scholarship (MIRS) and Melbourne International Fee Remission Scholarship (MIFRS). His Ph.D. thesis was nominated for CORE John Makepeace Bennett Award for the Australasian Distinguished Doctoral Dissertation and John Melvin Memorial Scholarship for the Best Ph.D. thesis in Engineering.His research interests include scheduling and resource provisioning mechanisms for distributed systems. Currently, he is working on resource management in Software-Defined Networks (SDN)-enabled Cloud Computing.
Submission deadline: Extended to July 14, 2017
Notification of acceptance: Extended to August 21, 2017
Announced on August 14, 2017
Camera-ready deadline: September 15, 2017 Extended to September 22nd