Lectures / Seminars

Detecting all repeated patterns in sequences using suffix arrays

Pattern detection is a topic of great interest for many sciences. Suffix trees and suffix arrays are the most commonly used data structures for this purpose. For such purposes we have created the novel algorithm ARPaD, which uses the suffix array under a new perspective. With the use of the recursive algorithm ARPaD over the actual suffix array we can perform a fast analysis of any kind of sequences and detect all repeated patterns in linear time complexity.

Modelling of body-centric communications channel: Investigation of the influence of wearable antennas and user mobility

In recent years the amount of wearable devices on human body has been dramatically increased with various functionalities being served in many areas. The trend of incorporating more and more wearable devices in our everyday life has come to fulfil the need for closer and accurate monitoring and preventing in healthcare, but also for entertainment, fitness and convenience purposes.

Optimal Power Allocation in Block Fading Gaussian Channels with Secrecy Constraints

Physical layer security (PLS) investigates the potential of exploiting impairments of real communication channels, such as fading and noise, in order to achieve confidentiality in information exchange. PLS was pioneered by Wyner, who introduced the wiretap channel and established the possibility of creating perfectly secure communication links without relying on private (secret) keys. Recently, considerable efforts have been channelled to generalizing this result to the wireless fading channel and to multi-user scenarios.

Free Online Webinar entitled "Introduction to Vocational Guidance with the use of ICTs" with the participation of more than 350 people

The Telecoms Lab-Net Media Lab organized a free online webinar entitled "Introduction to Vocational Guidance with the use of ICTs" with the participation of more than 350 people.

The pyramid quantized Weisfeiler-Lehman graph representation

Graphs are a technique to represent data with inherited structure. Despite the signi cant progress in graph kernels, existing graph kernels focus on either unlabeled or discretely labeled graphs, while efficient and expressive representation and comparison of graphs with continuous high-dimensional vector labels, remains an open research problem. We introduce a novel method, the pyramid quantized Weisfeiler-Lehman graph representation to tackle the graph comparison problem for continuous vector labeled graphs.

Multimedia Concept Detection in Large-scale Settings with Laplacian Eigenmaps

The presentation describes a semi-supervised framework based on a fast and efficient feature representation with a highly scalable learning approach, achieving high accuracy and substantial gains in computational cost. The framework makes possible to implement a semi-supervised approach in large-scale settings.

Computational Justice in Socio-Technical Systems

Many open distributed systems and networks have to deal with a resource allocation problem compounded by an economy of scarcity and opportunistic non-compliance. We have approached this problem from the perspective of self-organising multi-agent systems, using electronic institutions founded on the institutional design principles of Nobel Prize winner Elinor Ostrom.

Pattern mining in multi-relational data

"Pattern mining research has so far mostly focused on mining a single relation. The most representative example of such methods is frequent itemset mining which aims at discovering frequently co-occurring items from a relation of items and market basket transactions. However, most real world datasets are usually richer, containing more than one relation and more than two entity types (ex. characteristics of items).Therefore pattern mining methods that target multi-relational data are equally important.

N-gram graphs and proximity graphs: bringing summarization, machine learning and bioinformatics to the same neighbourhood



N-gram graphs offer a generic representation and a related framework for the representation and handling of symbol sequences. They encode neighbourhood information between items expressing both local and global characteristics of the sequences in a manner that supports uncertainty. They have been applied successfully to several tasks, such as news summarization (NewSum method) and news summarization evaluation (AutoSummENG, MeMoG, NPowER methods). N-gram graphs can also co-exist with and empower existing machine learning algorithms in the vector space.

Event Recognition for Unobtrusive Assisted Living

Developing intelligent systems towards automated clinical monitoring and assis-tance for the elderly is attracting significant attention. Age-related demographic trends in most western countries and increasing health-care costs, indicate a need for robust telehealth solutions which shall prolong seniors’ independent living. A key requirement in most ambient intelligence and assisted living applications is unobtrusiveness: Monitoring should not intervene with daily activities, so that the user feels comfortable and sensor data are collected naturally and in an unbiased fashion.

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