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Paschos G.S.,Informatics and Telematics Institute | Fragiadakis C.,University of Thessaly | Georgiadis L.,Aristotle University of Thessaloniki | Tassiulas L.,University of Thessaly
Proceedings - IEEE INFOCOM | Year: 2013

We study an 1-hop broadcast channel with two receivers. Due to overhearing channels, the receivers have side information which can be leveraged by interflow network coding techniques to provide throughput increase. In this setup, we consider two different control mechanisms, the deterministic system, where the contents of the receivers' buffers are announced to the coding node via overhearing reports and the stochastic system, where the coding node makes stochastic control decisions based on statistics and the performance is improved via NACK messages. We study the minimal evacuation times for the two systems and obtain analytical expressions of the throughput region for the deterministic and the code-constrained region for the stochastic. We show that maximum performance is achieved by simple XOR policies. For equal transmission rates r1 = r2, the two regions are equal. If r1 ≠ r2, we showcase the tradeoff between throughput and overhead. © 2013 IEEE.

Papadopoulos S.,Informatics and Telematics Institute | Papadopoulos S.,Aristotle University of Thessaloniki | Kompatsiaris Y.,Informatics and Telematics Institute | Vakali A.,Aristotle University of Thessaloniki | Spyridonos P.,Aristotle University of Thessaloniki
Data Mining and Knowledge Discovery | Year: 2012

The proposed survey discusses the topic of community detection in the context of Social Media. Community detection constitutes a significant tool for the analysis of complex networks by enabling the study of mesoscopic structures that are often associated with organizational and functional characteristics of the underlying networks. Community detection has proven to be valuable in a series of domains, e.g. biology, social sciences, bibliometrics. However, despite the unprecedented scale, complexity and the dynamic nature of the networks derived from Social Media data, there has only been limited discussion of community detection in this context. More specifically, there is hardly any discussion on the performance characteristics of community detection methods as well as the exploitation of their results in the context of real-world web mining and information retrieval scenarios. To this end, this survey first frames the concept of community and the problem of community detection in the context of Social Media, and provides a compact classification of existing algorithms based on their methodological principles. The survey places special emphasis on the performance of existing methods in terms of computational complexity and memory requirements. It presents both a theoretical and an experimental comparative discussion of several popular methods. In addition, it discusses the possibility for incremental application of the methods and proposes five strategies for scaling community detection to real-world networks of huge scales. Finally, the survey deals with the interpretation and exploitation of community detection results in the context of intelligent web applications and services. © The Author(s) 2011.

Petkos G.,Informatics and Telematics Institute | Papadopoulos S.,Informatics and Telematics Institute | Kompatsiaris Y.,Informatics and Telematics Institute
Proceedings of the 2nd ACM International Conference on Multimedia Retrieval, ICMR 2012 | Year: 2012

A large variety of features can be extracted from raw multimedia items. Moreover, in many contexts, like in the case of multimedia uploaded by users of social media platforms, items may be linked to metadata that can be very useful for a variety of analysis tasks. Nevertheless, such features are typically heterogeneous and are difficult to combine in a unified representation that would be suitable for analysis. In this paper, we discuss the problem of clustering collections of multimedia items with the purpose of detecting social events. In order to achieve this, a novel multimodal clustering algorithm is proposed. The proposed method uses a known clustering in the currently examined domain, in order to supervise the multimodal fusion and clustering procedure. It is tested on the MediaEval social event detection challenge data and is compared to a multimodal spectral clustering approach that uses early fusion. By taking advantage of the explicit supervisory signal, it achieves superior clustering accuracy and additionally requires the specification of a much smaller number of parameters. Moreover, the proposed approach has wider scope; it is not only applicable to the task of social event detection, but to other multimodal clustering problems as well. Copyright © 2012 ACM.

Kordelas G.,Informatics and Telematics Institute | Daras P.,Informatics and Telematics Institute
Pattern Recognition | Year: 2010

Viewpoint independent recognition of free-form objects and estimation of their exact position are a complex procedure with applications in robotics, artificial intelligence, computer vision and many other scientific fields. In this paper a novel approach is presented that addresses recognition of objects lying in highly cluttered and occluded scenes. The proposed procedure relies on distance maps, which are extracted and stored off-line for each of the 3D objects that might be contained in the scene. During the on-line recognition procedure distance maps are extracted from the scene. Greyscale images, derived from scene's distance maps, are matched with those of the object under recognition by applying similarity measures to the descriptors that are extracted from the images. The similarity is then estimated from image patches, which are defined using the SIFT descriptor in an appropriate way. After finding the best similarities the position of the object in the scene is estimated. This process is repeated until all objects are successfully recognized. Multiple experiments, which were performed on both 2.5D synthetic and real scenes, proved that the proposed method is robust and highly efficient to a satisfactory degree of occlusion and clutter. © 2010 Elsevier Ltd. All rights reserved.

Satsiou A.,Informatics and Telematics Institute | Tassiulas L.,Informatics and Telematics Institute
IEEE Transactions on Parallel and Distributed Systems | Year: 2010

In this paper, we study p2p systems, where peers have to share their available resources between their own and other peers' needs. One such example is a system of peers who use their capacity-limited access links both for their upstream and downstream connections. In the selfish approach, each peer would like to exploit the full capacity of his access link only for his downloads. However, if all peers acted selfishly, the system would collapse. In order to motivate peers to cooperate, we propose a distributed reputation-based system according to which peers earn reputation analogous to their contributions. In this way, each peer has to trade off the capacity he will dedicate for uploading in order to increase his reputation and therefore his revenue and the capacity he will dedicate for his downloads. All peers act rationally, trying to maximize their utility. Our proposed policies lead rational peers to cooperation while promoting fairness, as peers receive resources in proportion to their contributions. Our policies outperform existing work in this area in which the slowest link becomes the bottleneck of a heterogeneous system of different link capacity peers. On the contrary, no such bottleneck appears when our policies are used, improving the performance of the system. Finally, we apply our reputation-based approach in a BitTorrent-like file sharing system and we highlight the potential performance gains. © 2010 IEEE.

Kalfas G.,Informatics and Telematics Institute | Pleros N.,Informatics and Telematics Institute
Journal of Lightwave Technology | Year: 2010

We present an agile and medium-transparent Medium Access Control (MT-MAC) protocol for seamless and dynamic capacity allocation over both optical and wireless transmission media in 60 GHz broadband Radio-Over-Fiber (RoF) networks. Medium transparency is achieved by means of parallelism between two simultaneously running contention periods and through nesting of wireless user-specific dataframes within Remote Antenna Unit (RAU)-specific optical Superframes. The first contention period reports on the traffic requesting RAUs and decides about the wavelength assignments, whereas the second contention period arbitrates traffic between wireless clients served by the same RAU. Seamless service delivery is completed by RAU-dedicated optical Superframes, each one incorporating multiple user-specific and time-division multiplexed dataframes that are opto-electronically converted at the RAU site and get transmitted wirelessly down to each end-user. The proposed MAC protocol is demonstrated to operate successfully both in RoF-over-bus as well as in RoF-over-Passive Optical Network (PON) architectures requiring only minor variations for getting adapted to the network topology. Its performance for both network topologies is evaluated through simulations for different number of end-users, different loads and network node densities and for bit-rates up to 3 Gb/s, both for a Poisson and for a burst-mode traffic model. Successful operation is demonstrated for all different cases, confirming its agility and showing that extended range 60 GHz LAN areas between wireless users even without line of sight conditions can be obtained. Moreover, the high throughput and low latency values for non-saturated network conditions reveal its potential for transforming broadband 60 GHz picocellular networks into highly effective RoF-enabled 60 GHz Wireless LANs even for high-bandwidth and time-sensitive applications like High-Definition video streaming. © 2010 IEEE.

Gitzenis S.,Informatics and Telematics Institute | Paschos G.S.,Informatics and Telematics Institute | Tassiulas L.,University of Thessaly
Proceedings - IEEE INFOCOM | Year: 2012

A key consideration in novel communication paradigms in multihop wireless networks regards the scalability of the network. We investigate the case of nodes making random requests on content stored in multiple replicas over the wireless network. We show that, in contrast to the conventional paradigm of random communicating pairs, multihop communication is a sustainable scheme for certain values of file popularity, cache and network size. In particular, we formulate the joint problem of replication and routing and compute an order optimal solution. Assuming a Zipf file popularity distribution, we vary the number of files M in the system as a function of the nodes N, let both go to infinity and identify the scaling regimes of the required link capacity, from O(√N) down to O(1). © 2012 IEEE.

Briassouli A.,Informatics and Telematics Institute | Kompatsiaris I.,Informatics and Telematics Institute
Proceedings of the IEEE International Conference on Computer Vision | Year: 2011

The behavior of crowds is of interest in many applications, but difficult to analyze due to the complexity of the activities taking place, the number of people moving in the scene and occlusions occurring between them. This work focuses on the problem of detecting new events in crowds using an original approach that is based on properties of the data in the Fourier domain, which leads to computationally effective and fast solutions that lead to accurate results without requiring data modeling or extensive training. The PETS2009 dataset has been used for benchmarking algorithms developed for analyzing crowd behavior, such as recognizing events in them. Experiments on the PETS 2009 dataset show that the proposed approach achieves the same or better results than existing techniques in detecting new events, while requiring almost no training samples. Extensions for accurate recognition and dealing with more complex events are also proposed as areas of future research. © 2011 IEEE.

Sidiropoulos P.,Informatics and Telematics Institute | Vrochidis S.,Informatics and Telematics Institute | Kompatsiaris I.,Informatics and Telematics Institute
Pattern Recognition | Year: 2011

This paper proposes a method for binary image retrieval, where the black-and-white image is represented by a novel feature named the adaptive hierarchical density histogram, which exploits the distribution of the image points on a two-dimensional area. This adaptive hierarchical decomposition technique employs the estimation of point density histograms of image regions, which are determined by a pyramidal grid that is recursively updated through the calculation of image geometric centroids. The extracted descriptor combines global and local properties and can be used in variant types of binary image databases. The validity of the introduced method, which demonstrates high accuracy, low computational cost and scalability, is both theoretically and experimentally shown, while comparison with several other prevailing approaches demonstrates its performance. © 2010 Elsevier Ltd. All rights reserved.

Tsiotsios C.,Imperial College London | Petrou M.,Informatics and Telematics Institute
Pattern Recognition | Year: 2013

Anisotropic diffusion filtering is highly dependent on some crucial parameters, such as the conductance function, the gradient threshold parameter and the stopping time of the iterative process. The various alternative options at each stage of the algorithm are examined and evaluated and the best choice is selected. An automatic stopping criterion is proposed, that takes into consideration the quality of the preserved edges as opposed to just the level of smoothing achieved. The proposed scheme is evaluated with the help of real and simulated images, and compared with other state of the art schemes using objective criteria. © 2012 Elsevier Ltd.

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