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.

Abstract:
The cache coherence protocol is an important but notoriously complicated part of a multicore processor. Typical protocols are far too complicated to verify completely and thus industry relies on extensive testing in hopes of uncovering bugs. In this work, we propose a verification-aware approach to protocol design, in which we design scalable protocols such that they can be completely formally verified. Rather than innovate in verification techniques, we use existing verification techniques and innovate in the design of the protocols. We present two design methodologies that, if followed, facilitate verification of arbitrarily scaled protocols. We discuss the impact of the constraints that must be followed, and we highlight possible future directions in verification-aware microarchitecture.

Speaker Bio:
Daniel J. Sorin is the Addy Professor of Electrical and Computer Engineering at Duke University. His research interests are in computer architecture, with a focus on fault tolerance, verification, and memory system design. He is the author of “Fault Tolerant Computer Architecture” and a co-author of “A Primer on Memory Consistency and Cache Coherence.” He is the recipient of a SICSA Distinguished Visiting Fellowship, a National Science Foundation Career Award, and Duke’s Imhoff Distinguished Teaching Award. He received a PhD and MS in electrical and computer engineering from the University of Wisconsin, and he received a BSE in electrical engineering from Duke University.

Abstract:
Plan and goal recognition is the task of inferring the plan and goal of an agent through the observation of its actions and its environment and has a number of applications on computer-human interaction, assistive technologies and surveillance.
Although such techniques using planning domain theories have developed a number of very accurate and effective techniques, they often rely on assumptions of full observability and noise-free observations.
These assumptions are not necessarily true in the real world, regardless of the technique used to translate sensor data into symbolic logic-based observations.
In this work, we develop plan recognition techniques, based on classical planning domain theories, that can cope with observations that are both incomplete and noisy and show how they can be applied to sensor data processed through deep learning techniques.
We evaluate such techniques on a kitchen video dataset, bridging the gap between symbolic goal recognition and real-world data.

Speaker Bio:
Dr. Felipe Meneguzzi is a researcher on multiagent systems, normative reasoning and automated planning. He is currently an associate professor at Pontifícia Universidade Católica do Rio Grande do Sul (PUCRS). Prior to that appointment he was a Project Scientist at the Robotics Institute at Carnegie Mellon University in the US. Felipe got his PhD at King’s College London in the UK and an undergraduate and masters degree at PUCRS in Brazil. He received the 2016 Google Research Awards for Latin America, and was one of four runners up to 2013 Microsoft Research Awards. His current research interests include plan recognition, hybrid planning and norm reasoning.

Slides from the talk

Abstract:
An email client. An instant messenger. A real-time financial market data viewer and news reader. A portfolio viewer. A note taker, file manager, media viewer, flight planner, restaurant finder… All built into one secure mobile application. On 4 different mobile operating systems. Does this sound challenging?
Mark from Bloomberg’s Mobile team will discuss how conventional development tools and techniques scale poorly when faced with this challenge, and how Bloomberg tackles the problem.

Speaker Bio:
Mark Olleson is a software engineer working in Bloomberg’s Mobile Professional team. Mark start developing iOS apps around the time the original iPad launched, and since has worked on projects which share common characteristics: scale and complexity. Today he specialises in large-scale and cross-platform mobile-app technology.