IT-Compliance und IT-Governance

Ohne Zusammenfassung     

A Dynamic Priority MAC Protocol for Time-Bounded Services in Wireless Networks

Wireless Local Area Networks have gained popularity at an unprecedented rate over the last few years. However, as the spectrum of applications they are called to support broadens, their inefficiency in meeting the diverse requirements of a wider range of applications becomes evident. Most existing access mechanisms cannot provide Quality-of-Service (QoS) assurances. Even those that are QoS aware can only provide relative service differentiation. In this work, we propose a dynamic priority medium access scheme to provide time-bounded services. By approximating an ideal Earliest Deadline First (EDF) scheduler, the proposed scheme can offer delay and delay jitter assurances while achieving high medium utilization. Analytical studies and simulation experiments document and confirm the positive characteristics of the proposed mechanism. Orestis Tsigkas received his Diploma in electrical and computer engineering from the Aristotle University of Thessaloniki, Greece in 2002. He is currently working towards his Ph.D. degree in the same department. His research interests include medium access, as well as quality-of-service provisioning. Fotini-Niovi Pavlidou received the Ph.D. degree in electrical engineering from the Aristotle University of Thessaloniki, Greece, in 1988 and the Diploma in mechanical-electrical engineering in 1979 from the same institution. She is currently a Professor at the Department of Electrical and Computer Engineering at the Aristotle University engaged in teaching for the under- and post-graduate program in the areas of mobile communications and telecommunications networks. Her research interests are in the field of mobile and personal communications, satellite communications, multiple access systems, routing and traffic flow in networks and QoS studies for multimedia applications over the Internet. She is being involved with many national and international projects in these areas (Tempus, COST, Telematics,IST) and she has been chairing the European COST262 Action on “Spread Spectrum Systems and Techniques for Wired and Wireless Communications”. She has served as member of the TPC in many IEEE/IEE conferences and she has organized/chaired some conferences like, the “IST Mobile Summit 2002”, the 6th “International Symposium on Power Lines Communications-ISPLC2002”, the “International Conference on Communications-ICT1998” etc. She is a permanent reviewer for many IEEE/IEE journals. She has published about 80 papers in refereed journals and conferences. She has served as guest-editor on special issues as: “Mobile Ad Hoc Networks (MANETs): Standards, Research, Applications” in the International Journal of Wireless Information Networks and “Power Line Communications and Applications” in the International Journal on Communications Systems. She is a senior member of IEEE, currently chairing the joint IEEE VT&AES Chapter in Greece.     

Detection of single photoluminescent diamond nanoparticles in cells and study of the internalization pathway

Diamond nanoparticles are promising photoluminescent probes for tracking intracellular processes, due to embedded, perfectly photostable color centers. In this work, the spontaneous internalization of such nanoparticles (diameter 25 nm) in HeLa cancer cells is investigated by confocal microscopy and time-resolved techniques. Nanoparticles are observed inside the cell cytoplasm at the single-particle and single-color-center level, assessed by time-correlation intensity measurements. Improvement of the nanoparticle signal-to-noise ratio inside the cell is achieved using a pulsed-excitation laser and time-resolved detection taking advantage of the long radiative lifetime of the color-center excited state as compared to cell autofluorescence. The internalization pathways are also investigated, with endosomal marking and colocalization analyses. The low colocalization ratio observed proves that nanodiamonds are not trapped in endosomes, a promising result in prospect of drug delivery by these nanoparticles. Low cytotoxicity of these nanoparticles in this cell line is also shown.     

Discovering recurring activity in temporal networks

Recent advances in data-acquisition technologies have equipped team coaches and sports analysts with the capability of collecting and analyzing detailed data of team activity in the field. It is now possible to monitor a sports event and record information regarding the position of the players in the field, passing the ball, coordinated moves, and so on. In this paper we propose a new method to analyze such team activity data. Our goal is to segment the overall activity stream into a sequence of potentially recurrent modes, which reflect different strategies adopted by a team, and thus, help to analyze and understand team tactics. We model team activity data as a temporal network, that is, a sequence of time-stamped edges that capture interactions between players. We then formulate the problem of identifying a small number of team modes and segmenting the overall timespan so that each segment can be mapped to one of the team modes; hence the set of modes summarizes the overall team activity. We prove that the resulting optimization problem is \(\mathrm {NP}\)-hard, and we discuss its properties. We then present a number of different algorithms for solving the problem, including an approximation algorithm that is practical only for one mode, as well as heuristic methods based on iterative and greedy approaches. We benchmark the performance of our algorithms on real and synthetic datasets. Of all methods, the iterative algorithm provides the best combination of performance and running time. We demonstrate practical examples of the insights provided by our algorithms when mining real sports-activity data. In addition, we show the applicability of our algorithms on other types of data, such as social networks.     

Advection‐Based Function Matching on Surfaces

A tangent vector field on a surface is the generator of a smooth family of maps from the surface to itself, known as the flow. Given a scalar function on the surface, it can be transported, or advected, by composing it with a vector field's flow. Such transport is exhibited by many physical phenomena, e.g., in fluid dynamics. In this paper, we are interested in the inverse problem: given source and target functions, compute a vector field whose flow advects the source to the target. We propose a method for addressing this problem, by minimizing an energy given by the advection constraint together with a regularizing term for the vector field. Our approach is inspired by a similar method in computational anatomy, known as LDDMM, yet leverages the recent framework of functional vector fields for discretizing the advection and the flow as operators on scalar functions. The latter allows us to efficiently generalize LDDMM to curved surfaces, without explicitly computing the flow lines of the vector field we are optimizing for. We show two approaches for the solution: using linear advection with multiple vector fields, and using non‐linear advection with a single vector field. We additionally derive an approximated gradient of the corresponding energy, which is based on a novel vector field transport operator. Finally, we demonstrate applications of our machinery to intrinsic symmetry analysis, function interpolation and map improvement.     

Malware classification based on call graph clustering

Each day, anti-virus companies receive tens of thousands samples of potentially harmful executables. Many of the malicious samples are variations of previously encountered malware, created by their authors to evade pattern-based detection. Dealing with these large amounts of data requires robust, automatic detection approaches. This paper studies malware classification based on call graph clustering. By representing malware samples as call graphs, it is possible to abstract certain variations away, enabling the detection of structural similarities between samples. The ability to cluster similar samples together will make more generic detection techniques possible, thereby targeting the commonalities of the samples within a cluster. To compare call graphs mutually, we compute pairwise graph similarity scores via graph matchings which approximately minimize the graph edit distance. Next, to facilitate the discovery of similar malware samples, we employ several clustering algorithms, including k-medoids and Density-Based Spatial Clustering of Applications with Noise (DBSCAN). Clustering experiments are conducted on a collection of real malware samples, and the results are evaluated against manual classifications provided by human malware analysts. Experiments show that it is indeed possible to accurately detect malware families via call graph clustering. We anticipate that in the future, call graphs can be used to analyse the emergence of new malware families, and ultimately to automate implementation of generic detection schemes.     

An adaptive medium access control protocol using m-ary tree algorithms for quality-of-service support in single-cell ad hoc networks

Concurrent with the rapid expansion of wireless networks is an increasing interest in providing Quality-of-Service (QoS) support to them. As a consequence, a number of medium access control protocols has been proposed which aims at providing service differentiation at the distributed wireless medium access layer. However, most of them provide only average performance assurances. We argue that average performance guarantees will be inadequate for a wide range of emerging multimedia applications and “per-flow” service assurances must be provided instead. Based on m-ary tree algorithms, we propose an adaptive and distributed medium access algorithm for single-cell ad hoc networks to provide “per-flow” service assurances to flows whose QoS requirement can be expressed as a delay requirement. Both analytical and simulation experiments are conducted to assess the performance of the proposed scheme.