Seminar Series on Advances in Telecommunications, Networking and Computing

This seminar series is organized by the Department of Telecommunications to provide unique opportunities to meet internationally recognized leading experts in the fields of Telecommunications, Networking and Computing. The lectures will be given in English by prominent foreign researchers from academia and industry, and they will be open to ALL interested colleagues, PhD students, and students. There is no fixed schedule for the seminar, but the lectures will be organized based on the availability of the invited lecturers, and they will be announced via various mailing lists. In addition, slides and up-to-date information on the program will be published on this site.

For further information, please, contact Dr. Levente Buttyán, Program Chair by e-mail (buttyan (at) hit.bme.hu) or telephone (+36 1 463 1803).

Past years: 2009, 2010, 2011

Program in 2012

Planned seminars:

Google calendar for ATNC seminars

Program in details

Acoustic development of electric power assisted steering systems

Abstract: ThyssenKrupp Presta is one of the biggest steering system supplier for the automotive industry. Its wide product portfolio includes steering columns, intermediate shafts and electric power assisted steering systems like Rack-EPAS and Col-PAS. Although the company has a long history and tremendous experience in cold-forging and producing steel parts, the development of a complete mechatronic system raised new challanging fields like acoustics. This lecture aims to give an overview about the latest techniques and methods used in the automotive industry to measure, evaluate and to judge the acoustic behaviour of a steering system, and show the upcoming challanges on this field considering the newest trends in the industry.

Short bio: Tibor Kimpián received his M.S. degree in electrical engineering from Budapest University of Technology and Economics in 2006. From 2006 to 2009 he had worked for ThyssenKrupp Presta Hungary Ltd and he was a PhD student at the same time at the same university. This joint programme gave him the opportunity to carry out scientific research on permanent magnet synchronous motor drives including the analysis of its torsional vibration problems. Currently he is project engineer for acoustics at ThyssenKrupp Presta in Eschen, Liechtenstein.

Budget-Limited Multi-Armed Bandits: Theory and Its Applications in Telecommunications

Abstract: Decision making under uncertainty is one of the most important challenges within the research field of artificial intelligence, as they present many everyday situations that agents have to face. Within these situations, an agent has to choose from a set of options, whose payoff is uncertain (i.e. unknown and nondeterministic) to the agent. Common to such decision making problems is the need of balancing between exploration and exploitation, where the agent, in order to maximise its total payoff, must decide whether to choose the option expected to provide the best payoff (exploitation) or to try an alternative option for potential future benefit (exploration).
Among many decision under uncertainty abstractions, multi–armed bandits (MAB) are perhaps one of the most common and best studied, as they present one of the clearest examples of the trade–off between exploration and exploitation. The MAB model consists of a slot machine with K arms, each of which has a different and unknown expected reward. The goal of the agent (player) is to repeatedly pull the optimal arm (i.e. the arm with the highest expected reward) to maximise the expected total reward. However, the agent does not know the rewards for each arm, so it must sample them in order to learn which is the optimal one. In other words, in order to choose the optimal arm the agent first has to estimate the mean rewards of all of the arms.
In this talk, I will introduce a novel MAB model, the budget-limited MAB, in which pulling an arm is costly and is constrained by a budget limit. I will propose a set of pulling algorithms, and analyse their performance. In addition, I will demonstrate the usefulness of the model through a variety of applications in different fields, such as: decentralised coordination with UAVs, expert crowdsourcing, or financial computation. In the last part of my talk, I will focus on the possibilities of applying the budget-limited MAB model to the field of telecommunications. These applications include, but are not limited to, the followings: data collection in multi-sensor networks, channel allocation in UWB, and internet security.

Short bio: Long Tran-Thanh received his M.Sc degree in IT engineering from Department of Telecommunications, Budapest University of Technology and Economics (BME), in 2007. Following this, he started his PhD studies in electrical engineering with prof. Janos Levendovszky at the Department of Telecommunications, BME, but postponed it for a leave to Southampton in 2008 (the thesis submission for this PhD is expected to be in late 2012). In Southampton he joined the Agents, Interactions, Complexity (AIC) group of prof. Nick Jennings as a PhD student in artificial intelligence. After his thesis defence in 2011, he continued working at AIC as a research fellow.

Social Apps' Data Practices and Privacy Permission Dialogues: A Field Study

Abstract: Several studies have documented the constantly evolving privacy practices of social networking sites and users' misunderstandings about them. Justifiably, users have criticized the interfaces to "configure" their privacy preferences as opaque, disjointed, uninformative and ultimately ineffective. The same problems have also plagued the constantly growing economy of third-party applications and their equally troubling authentication and authorization dialogues with important options being unavailable at installation time and/or widely distributed across the sites' privacy options pages. In this talk, I will discuss the results of a field study of the current authorization dialogue for the currently dominant social networking site as well as four canonical designs of installation dialogues which are based on the internationally favored Fair Information Practice Principles (FIPPs). In particular, we study and document the effectiveness of installation-time configuration and awareness-enhancing interface changes when 250 users investigate our experimental application in the privacy of their homes.

Short bio: Dr. Grossklags is an Assistant Professor at the College of Information Sciences and Technology at the Pennsylvania State University. He is affiliated with the Security and Risk Analysis program and a member of the steering committee of the Center for the Study of Global Financial Stability. Previously, he served as a Postdoctoral Research Associate at the Center for Information Technology Policy, and as a Lecturer of Computer Science at Princeton University. In 2009, he completed his doctoral dissertation at UC Berkeley's School of Information. While at UC Berkeley, he also obtained master's degrees in Computer Science, and Information Management and Systems. He is studying information privacy, security, technology policy and networked interactions from a theoretical, empirical and practical perspective. Specifically, Dr. Grossklags is motivated to contribute to a better understanding of the current and future marketplace for personal and corporate information, and improved designs of the underlying evolving security infrastructure. His academic work is very cross-disciplinary and utilizes analytic, empirical and experimental methodologies.

Scalable Performance Analysis of Massively Parallel Stochastic Systems

Abstract: Performance analysis has always suffered from the state-space explosion problem which directly prohibits the scalability of stochastic modelling as a tool for resolving resource provisioning and quality of service questions in massively parallel computer and communication systems. This is especially true when applied to the recent ubiquitous breed of distributed and peer-to-peer systems. One way around these scalability limitations are asymptotic techniques formally justified by functional laws of large numbers often termed variously "fluid" or "mean-field" analysis. These techniques have their roots in classical heavy-traffic analysis in the context of queueing networks, and also borrow from ideas in chemistry and biology. Such approaches have recently experienced something of a revival in the context of general massive interacting computational systems. In this talk, we will introduce these approaches and showcase some of the methods and the results which can be obtained.

Short bio: http://www.doc.ic.ac.uk/~rh/cv