Abstract:

Many real-world systems are most naturally modelled by “multi-layer” networks, which allow for different types of connections between entities; it is therefore important to develop efficient algorithms to extract information from such networks. However, most existing results concerning the structural properties of graphs/networks which allow us to solve NP-hard problems efficiently consider only the case of a “single-layer” graph (in which each pair of vertices is either adjacent or not). A natural question to ask is whether, if each individual layer has well-understood structure which allows the design of efficient algorithms, we can still exploit this structure to solve problems on the combined, multi-layer network. We address this question for the specific problem of counting small substructures in the network: in most cases the problem becomes intractable on the combined network, but we identify one case in which structural restrictions on the individual layers can be exploited effectively.

This is joint work with Jessica Enright (University of Stirling).

Speaker Bio:

Kitty Meeks obtained her PhD from the University of Oxford in 2013, and from 2012 to 2014 worked as a Postdoctoral Research Assistant at Queen Mary University of London. She joined the University of Glasgow in 2014, initially to the School of Mathematics and Statistics, before moving across the road to the School of Computing Science in 2016. She currently holds a Royal Society of Edinburgh Personal Research Fellowship for the project “Exploiting Realistic Graph Structure”.

Abstract:

Usable security is about exploring the relationship between the tools
which are supposed to keep people safe and the ways that people interact
with them. In this talk, I will be discussing two of my recent projects:
URL readability and reasons for avoiding software updates. URLs are a
nearly ubiquitous method of telling another person where to find
content. They are used extensively in emails, social networking and
other communications. The security community complains about people
clicking on fraudulent URLs, yet surprisingly little is known about how
people parse and interpret them. Similarly, software updates are
becoming a common feature of using a computing device, many of which
demand to be updated daily, if not hourly. Security experts agree that
installing updates is one of the best ways to stay safe, yet many people
avoid updating. I will discuss studies my lab has run on both of these
topics.

Speaker Bio:

Dr Kami Vaniea is a Lecturer at the University of Edinburgh studying
human factors of security and privacy. She heads the Technology
Usability Lab In Privacy and Security (TULIPS) which looks at many
different aspects of usability, prvaicy and security including
educational game design, internet of things, and software updating.
Previously Dr Vaniea was an Assistant Professor at Indiana University, a
post doc researcher at Michigan State University and completed her PhD
at Carnegie Mellon University.

Abstract:

This talk concentrates on our efforts over the years to make the harvesting of relevant data from mobile devices accurate and efficient, to allow on device data interpretation and to produce models able to interpret the data so that it can be exploited for a wide range of applications. In this sense I will describe specifics of our work which range from fitting mobile sensing inference on devices and how we are able to exploit local device heterogeneous computation resources efficiently to data analytics for mobile health and urban computing. I will discuss challenges and opportunities of the field throughout the talk.

Speaker Bio:

Cecilia Mascolo is a mother of a teenage daughter. She is also Full Professor of Mobile Systems in the Computer Laboratory, University of Cambridge, UK, a Fellow of Jesus College Cambridge and a Faculty Fellow at the Alan Turing Institute for Data Science in London. Prior joining Cambridge in 2008, she has been a faculty member in the Department of Computer Science at University College London. She holds a PhD from the University of Bologna. Her research interests are in human mobility modelling, mobile and sensor systems and networking and spatio-temporal data analysis. She has published in a number of top tier conferences and journals in the area and her investigator experience spans projects funded by Research Councils and industry. She has received numerous best paper awards and in 2016 was listed in “10 Women in Networking /Communications You Should Know”. She has served as steering, organizing and programme committee member of mobile, sensor systems, networking, data science conferences and workshops. She has delivered a number of keynote talks at conferences and workshops in the area of mobility, data science, pervasive computing and systems. She is Associate Editor in Chief for IEEE Pervasive Computing and sits on the editorial boards of IEEE Transactions on Mobile Computing, ACM Transactions on Sensor Networks and ACM Transactions on Interactive, Mobile, Wearable and Ubiquitous Technologies. More details at www.cl.cam.ac.uk/users/cm542.

Abstract:
Searching for complex objects (e.g. images, faces, audio or video), is an everyday problem in computer science, motivated by many applications. Efficient algorithms are demanded for reverse searching, also known as query by content, in large repositories. Current industrial solutions are ad hoc, domain-dependant, hardware intensive and have limited scaling. However, those disparate domains can be modelled, for indexing and searching, as a metric space. This model has been championed to become a solution to generic proximity searching problems. In practice, however, the metric space approach has been limited by the amount of main memory available.

In this talk we will explore the main ideas behind this technology, present a successful example in audio indexing and retrieval. The application scales well for large amounts of audio because the representation is quite compact and the full audio streams are not needed for indexing and searching.

Speaker Bio:
Edgar Chavez received his Phd from the Center for Mathematical Research in Guanajuato, Mexico in 1999. He founded the information retrieval group at Universidad Michoacana where he worked until 2012. After a brief period in the Institute of Mathematics in UNAM, he joined the computer science department in CICESE in 2013, where he founded the data science group. His main research interest include access and retrieval of data and data representation, such as fingerprints and point clouds. In 2009 he obtained the Thompson-Reuters award for having the most cited paper in computer science in Mexico and Latin America. In 2008 he co-funded, with Gonzalo Navarro, the conference Similarity Search and Applications, which is an international reference in the area. He has published more than 100 scientific contributions, with about 3500 citations in google scholar.